Articles | Volume 23, issue 16
https://doi.org/10.5194/acp-23-9071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-9071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2023
Research article |
| 19 Sep 2023
| 19 Sep 2023
Automated detection and monitoring of methane super-emitters using satellite data
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
GHGSat Inc., Montreal, Canada
Joannes D. Maasakkers
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Pieter Bijl
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Gourav Mahapatra
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Anne-Wil van den Berg
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
now at: Department of Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands
Sudhanshu Pandey
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
now at: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Alba Lorente
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Tobias Borsdorff
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Sander Houweling
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Daniel J. Varon
GHGSat Inc., Montreal, Canada
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Jason McKeever
GHGSat Inc., Montreal, Canada
Dylan Jervis
GHGSat Inc., Montreal, Canada
Marianne Girard
GHGSat Inc., Montreal, Canada
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
International Methane Emission Observatory, United Nations Environment Program, Paris, France
Javier Gorroño
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
Environmental Defense Fund, Amsterdam, the Netherlands
Daniel H. Cusworth
Carbon Mapper, Inc., Pasadena, CA, USA
Arizona Institute for Resilience, University of Arizona, Tucson, AZ, USA
Ilse Aben
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
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This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
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Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Qiurun Yu, Dylan Jervis, and Yi Huang
Atmos. Meas. Tech., 17, 3347–3366, https://doi.org/10.5194/amt-17-3347-2024, https://doi.org/10.5194/amt-17-3347-2024, 2024
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This study estimated the effects of aerosols on GHGSat satellite methane retrieval and investigated the performance of simultaneously retrieving aerosol and methane information using a multi-angle viewing method. Results suggested that the performance of GHGSat methane retrieval improved when aerosols were considered, and the multi-angle viewing method is insensitive to the satellite angle setting. This performance assessment is useful for improving future GHGSat-like instruments.
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024, https://doi.org/10.5194/amt-17-2777-2024, 2024
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We analyze Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the 2022 Nord Stream 2 methane leak and show how challenging this case is for usual data analysis methods. We provide customized calibrations for this Nord Stream 2 case and assess that no firm conclusion can be drawn from L8 or S-2B single overpasses. However, if we opportunistically assume that L8 and S-2B results are independent, we find an averaged L8 and S-2B combined methane leak rate of 502 ± 464 t h−1.
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024, https://doi.org/10.5194/amt-17-2625-2024, 2024
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Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data.
Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 2583–2593, https://doi.org/10.5194/amt-17-2583-2024, https://doi.org/10.5194/amt-17-2583-2024, 2024
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Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
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We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Emily Dowd, Alistair J. Manning, Bryn Orth-Lashley, Marianne Girard, James France, Rebecca E. Fisher, Dave Lowry, Mathias Lanoisellé, Joseph R. Pitt, Kieran M. Stanley, Simon O'Doherty, Dickon Young, Glen Thistlethwaite, Martyn P. Chipperfield, Emanuel Gloor, and Chris Wilson
Atmos. Meas. Tech., 17, 1599–1615, https://doi.org/10.5194/amt-17-1599-2024, https://doi.org/10.5194/amt-17-1599-2024, 2024
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We provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys of an active gas leak detected near Cheltenham, UK. GHGSat’s emission estimates broadly agree with the surface-based mobile survey and steps were taken to fix the leak, highlighting the importance of satellite data in identifying emissions and helping to reduce our human impact on climate change.
Javier Roger, Luis Guanter, Javier Gorroño, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 1333–1346, https://doi.org/10.5194/amt-17-1333-2024, https://doi.org/10.5194/amt-17-1333-2024, 2024
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Methane emissions can be identified using remote sensing, but surface-related structures disturb detection. In this work, a variation of the matched filter method that exploits a large fraction of the near-infrared range (1000–2500 nm) is applied. In comparison to the raw matched filter, it reduces background noise and strongly attenuates the surface-related artifacts, which leads to a greater detection capability. We propose this variation as a standard methodology for methane detection.
Jean-Philippe W. MacLean, Marianne Girard, Dylan Jervis, David Marshall, Jason McKeever, Antoine Ramier, Mathias Strupler, Ewan Tarrant, and David Young
Atmos. Meas. Tech., 17, 863–874, https://doi.org/10.5194/amt-17-863-2024, https://doi.org/10.5194/amt-17-863-2024, 2024
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We demonstrate the capabilities of the GHGSat satellite constellation to detect and quantify offshore methane emissions using a sun glint observation mode. Using this technique, we observe offshore methane emissions from space ranging from 180 kg h−1 to 84 000 kg h−1. We further assess the instrument performance in offshore environments, both empirically and using analytical modelling, and find that the detection limit varies with latitude and season.
Alana K. Ayasse, Daniel Cusworth, Kelly O'Neill, Justin Fisk, Andrew K. Thorpe, and Riley Duren
Atmos. Meas. Tech., 16, 6065–6074, https://doi.org/10.5194/amt-16-6065-2023, https://doi.org/10.5194/amt-16-6065-2023, 2023
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Methane is a powerful greenhouse gas, and a significant portion of methane comes from large individual plumes. Recently, airplane-mounted infrared technologies have proven very good at detecting and quantifying these plumes. In order to extract the methane signal from the infrared image, there are two widely used approaches. In this study, we assess the performance of both approaches using controlled-release experiments. We also examine the minimum detection limit of the infrared technology.
Daniel H. Cusworth, Andrew K. Thorpe, Charles E. Miller, Alana K. Ayasse, Ralph Jiorle, Riley M. Duren, Ray Nassar, Jon-Paul Mastrogiacomo, and Robert R. Nelson
Atmos. Chem. Phys., 23, 14577–14591, https://doi.org/10.5194/acp-23-14577-2023, https://doi.org/10.5194/acp-23-14577-2023, 2023
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Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we tasked two satellites to routinely observe CO2 emissions at 30 coal-fired power plants between 2021 and 2022. These results present the largest dataset of space-based CO2 emission estimates to date.
Tim A. van Kempen, Tim J. Rotmans, Richard M. van Hees, Carol Bruegge, Dejian Fu, Ruud Hoogeveen, Thomas J. Pongetti, Robert Rosenberg, and Ilse Aben
Atmos. Meas. Tech., 16, 4507–4527, https://doi.org/10.5194/amt-16-4507-2023, https://doi.org/10.5194/amt-16-4507-2023, 2023
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Validation of satellite measurements is essential for providing reliable and consistent products. In this paper, a validation method for TROPOMI-SWIR (Tropospheric Measurement Instrument in the short-wavelength infrared) is explored. TROPOMI-SWIR has been shown to be exceptionally stable, a necessity to explore the methodology. Railroad Valley, Nevada, is a prime location to perform the necessary measurements to validate the satellite measurements of TROPOMI-SWIR.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
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We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon
Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, https://doi.org/10.5194/amt-16-3787-2023, 2023
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We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Gijs Leguijt, Joannes D. Maasakkers, Hugo A. C. Denier van der Gon, Arjo J. Segers, Tobias Borsdorff, and Ilse Aben
Atmos. Chem. Phys., 23, 8899–8919, https://doi.org/10.5194/acp-23-8899-2023, https://doi.org/10.5194/acp-23-8899-2023, 2023
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We present a fast method to evaluate carbon monoxide emissions from cities in Africa. Carbon monoxide is important for climate change in an indirect way, as it is linked to ozone, methane, and carbon dioxide. Our measurements are made with a satellite that sees the entire globe every single day. This means that we can check from space whether the current knowledge of emission rates is up to date. We make the comparison and show that the emission rates in northern Africa are underestimated.
Ruosi Liang, Yuzhong Zhang, Wei Chen, Peixuan Zhang, Jingran Liu, Cuihong Chen, Huiqin Mao, Guofeng Shen, Zhen Qu, Zichong Chen, Minqiang Zhou, Pucai Wang, Robert J. Parker, Hartmut Boesch, Alba Lorente, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, https://doi.org/10.5194/acp-23-8039-2023, 2023
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We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
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We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Tobias Borsdorff, Teresa Campos, Natalie Kille, Kyle J. Zarzana, Rainer Volkamer, and Jochen Landgraf
Atmos. Meas. Tech., 16, 3027–3038, https://doi.org/10.5194/amt-16-3027-2023, https://doi.org/10.5194/amt-16-3027-2023, 2023
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ECMWF plans to assimilate TROPOMI CO with their CAMS-IFS model. This will constrain the total column and the vertical CO distribution of the model. To show this, we combine individual TROPOMI CO column retrievals with different vertical sensitivities and obtain a vertical CO concentration profile. We test the approach on three CO pollution events in comparison with CAMS-IFS simulations that do not assimilate TROPOMI CO data and in situ airborne measurements of the BB-FLUX campaign.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
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We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Anna Agustí-Panareda, Jérôme Barré, Sébastien Massart, Antje Inness, Ilse Aben, Melanie Ades, Bianca C. Baier, Gianpaolo Balsamo, Tobias Borsdorff, Nicolas Bousserez, Souhail Boussetta, Michael Buchwitz, Luca Cantarello, Cyril Crevoisier, Richard Engelen, Henk Eskes, Johannes Flemming, Sébastien Garrigues, Otto Hasekamp, Vincent Huijnen, Luke Jones, Zak Kipling, Bavo Langerock, Joe McNorton, Nicolas Meilhac, Stefan Noël, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Miha Razinger, Maximilian Reuter, Roberto Ribas, Martin Suttie, Colm Sweeney, Jérôme Tarniewicz, and Lianghai Wu
Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, https://doi.org/10.5194/acp-23-3829-2023, 2023
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We present a global dataset of atmospheric CO2 and CH4, the two most important human-made greenhouse gases, which covers almost 2 decades (2003–2020). It is produced by combining satellite data of CO2 and CH4 with a weather and air composition prediction model, and it has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. This dataset can be used for scientific studies in the field of climate change and the global carbon cycle.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, and Jochen Landgraf
Atmos. Meas. Tech., 16, 1597–1608, https://doi.org/10.5194/amt-16-1597-2023, https://doi.org/10.5194/amt-16-1597-2023, 2023
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In the TROPOMI methane data, there are few false methane anomalies that can be misinterpreted as enhancements caused by strong emission sources. These artefacts are caused by features of the underlying surfaces that are not well characterized in the retrieval algorithm. Here we improve the representation of the surface reflectance dependency with wavelength in the forward model, removing the artificial localized CH4 enhancements found in several locations like Siberia, Australia and Algeria.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
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This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023, https://doi.org/10.5194/acp-23-3325-2023, 2023
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We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
Adriana Bailey, Franziska Aemisegger, Leonie Villiger, Sebastian A. Los, Gilles Reverdin, Estefanía Quiñones Meléndez, Claudia Acquistapace, Dariusz B. Baranowski, Tobias Böck, Sandrine Bony, Tobias Bordsdorff, Derek Coffman, Simon P. de Szoeke, Christopher J. Diekmann, Marina Dütsch, Benjamin Ertl, Joseph Galewsky, Dean Henze, Przemyslaw Makuch, David Noone, Patricia K. Quinn, Michael Rösch, Andreas Schneider, Matthias Schneider, Sabrina Speich, Bjorn Stevens, and Elizabeth J. Thompson
Earth Syst. Sci. Data, 15, 465–495, https://doi.org/10.5194/essd-15-465-2023, https://doi.org/10.5194/essd-15-465-2023, 2023
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One of the novel ways EUREC4A set out to investigate trade wind clouds and their coupling to the large-scale circulation was through an extensive network of isotopic measurements in water vapor, precipitation, and seawater. Samples were taken from the island of Barbados, from aboard two aircraft, and from aboard four ships. This paper describes the full collection of EUREC4A isotopic in situ data and guides readers to complementary remotely sensed water vapor isotope ratios.
Javier Gorroño, Daniel J. Varon, Itziar Irakulis-Loitxate, and Luis Guanter
Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, https://doi.org/10.5194/amt-16-89-2023, 2023
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We present a methane flux rate retrieval methodology using the Sentinel-2 mission, validating the algorithm for different scenes and plumes. The detection limit is 1000–2000 kg h−1 for homogeneous scenes and temporally invariant surfaces and above 5000 kg h−1 for heterogeneous ones. Dominant quantification errors are wind-related or plume mask-related. For heterogeneous scenes, the surface structure underlying the methane plume can become a dominant source of uncertainty.
Srijana Lama, Sander Houweling, K. Folkert Boersma, Ilse Aben, Hugo A. C. Denier van der Gon, and Maarten C. Krol
Atmos. Chem. Phys., 22, 16053–16071, https://doi.org/10.5194/acp-22-16053-2022, https://doi.org/10.5194/acp-22-16053-2022, 2022
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Hydroxyl radical (OH) is the important chemical species that determines the lifetime of some greenhouse gases and trace gases. OH plays a vital role in air pollution chemistry. OH has a short lifetime and is extremely difficult to measure directly. OH concentrations derived from the chemistry transport model (CTM) have uncertainties of >50 %. Therefore, in this study, OH is derived indirectly using satellite date in urban plumes.
Zhan Zhang, Evan D. Sherwin, Daniel J. Varon, and Adam R. Brandt
Atmos. Meas. Tech., 15, 7155–7169, https://doi.org/10.5194/amt-15-7155-2022, https://doi.org/10.5194/amt-15-7155-2022, 2022
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This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, Andre Butz, Otto P. Hasekamp, Lianghai Wu, and Jochen Landgraf
Atmos. Meas. Tech., 15, 6585–6603, https://doi.org/10.5194/amt-15-6585-2022, https://doi.org/10.5194/amt-15-6585-2022, 2022
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The TROPOspheric Monitoring Instrument (TROPOMI) performs observations over ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the sun glint geometry the mirror-like reflection at the water surface provides a signal that is high enough to retrieve methane with high accuracy and precision. We present 4 years of methane concentrations over the ocean, and we assess its quality. We also show the importance of ocean observations to quantify total CH4 emissions.
Antje Inness, Ilse Aben, Melanie Ades, Tobias Borsdorff, Johannes Flemming, Luke Jones, Jochen Landgraf, Bavo Langerock, Philippe Nedelec, Mark Parrington, and Roberto Ribas
Atmos. Chem. Phys., 22, 14355–14376, https://doi.org/10.5194/acp-22-14355-2022, https://doi.org/10.5194/acp-22-14355-2022, 2022
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The Copernicus Atmosphere Monitoring Service (CAMS) provides daily global air quality forecasts to users worldwide. One of the species of interest is carbon monoxide (CO), an important trace gas in the atmosphere with anthropogenic and natural sources, produced by incomplete combustion, for example, by wildfires. This paper looks at how well CAMS can model CO in the atmosphere and shows that the fields can be improved when blending CO data from the TROPOMI instrument with the CAMS model.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
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We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
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We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Sara Martínez-Alonso, Merritt N. Deeter, Bianca C. Baier, Kathryn McKain, Helen Worden, Tobias Borsdorff, Colm Sweeney, and Ilse Aben
Atmos. Meas. Tech., 15, 4751–4765, https://doi.org/10.5194/amt-15-4751-2022, https://doi.org/10.5194/amt-15-4751-2022, 2022
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AirCore is a novel balloon sampling system that can measure, among others, vertical profiles of carbon monoxide (CO) from 25–30 km of altitude to near the surface. Our analyses of AirCore and satellite CO data show that AirCore profiles are suited for satellite data validation, the use of shorter aircraft vertical profiles in satellite validation results in small errors (1–3 percent points) mostly at 300 hPa and above, and the error introduced by clouds in TROPOMI land data is small (1–2 %).
Pieternel F. Levelt, Deborah C. Stein Zweers, Ilse Aben, Maite Bauwens, Tobias Borsdorff, Isabelle De Smedt, Henk J. Eskes, Christophe Lerot, Diego G. Loyola, Fabian Romahn, Trissevgeni Stavrakou, Nicolas Theys, Michel Van Roozendael, J. Pepijn Veefkind, and Tijl Verhoelst
Atmos. Chem. Phys., 22, 10319–10351, https://doi.org/10.5194/acp-22-10319-2022, https://doi.org/10.5194/acp-22-10319-2022, 2022
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Using the COVID-19 lockdown periods as an example, we show how Sentinel-5P/TROPOMI trace gas data (NO2, SO2, CO, HCHO and CHOCHO) can be used to understand impacts on air quality for regions and cities around the globe. We also provide information for both experienced and inexperienced users about how we created the data using state-of-the-art algorithms, where to get the data, methods taking meteorological and seasonal variability into consideration, and insights for future studies.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
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We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
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We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
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Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Jonas Hachmeister, Oliver Schneising, Michael Buchwitz, Alba Lorente, Tobias Borsdorff, John P. Burrows, Justus Notholt, and Matthias Buschmann
Atmos. Meas. Tech., 15, 4063–4074, https://doi.org/10.5194/amt-15-4063-2022, https://doi.org/10.5194/amt-15-4063-2022, 2022
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Sentinel-5P trace gas retrievals rely on elevation data in their calculations. Outdated or inaccurate data can lead to significant errors in e.g. dry-air mole fractions of methane (XCH4). We show that the use of inadequate elevation data leads to strong XCH4 anomalies in Greenland. Similar problems can be expected for other regions with inaccurate elevation data. However, we expect these to be more localized. We show that updating elevation data used in the retrieval solves this issue.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022, https://doi.org/10.5194/gmd-15-4555-2022, 2022
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Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
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This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
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Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Andreas Schneider, Tobias Borsdorff, Joost aan de Brugh, Alba Lorente, Franziska Aemisegger, David Noone, Dean Henze, Rigel Kivi, and Jochen Landgraf
Atmos. Meas. Tech., 15, 2251–2275, https://doi.org/10.5194/amt-15-2251-2022, https://doi.org/10.5194/amt-15-2251-2022, 2022
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This paper presents an extended H₂O/HDO total column dataset from short-wave infrared measurements by TROPOMI including cloudy and clear-sky scenes. Coverage is tremendously increased compared to previous TROPOMI HDO datasets. The new dataset is validated against recent ground-based FTIR measurements from TCCON and against aircraft measurements over the ocean. The use of the new dataset is demonstrated with a case study of a cold air outbreak in January 2020.
Elena Sánchez-García, Javier Gorroño, Itziar Irakulis-Loitxate, Daniel J. Varon, and Luis Guanter
Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, https://doi.org/10.5194/amt-15-1657-2022, 2022
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This study seeks to present the as-yet-unknown potential use of WorldView-3 for the mapping of methane point source emissions. The proposed retrieval methodology is based on the idea that the spectral channels not affected by methane can be used to predict the methane-affected band through regression analysis. The results show the precise location of 26 independent point emissions over different methane hotspot regions worldwide, which prove the game-changing potential that this mission entails.
Yousef Albuhaisi, Ype van der Velde, and Sander Houweling
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-55, https://doi.org/10.5194/bg-2022-55, 2022
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An important uncertainty in the modelling of methane emissions from natural wetlands is the wetland area. It is important to get the spatiotemporal covariance between the variables that drive methane emissions right for accurate quantification. Using high-resolution wetland and soil carbon maps, in combination with a simplified methane emission model that is coarsened in six steps from 0.005° to 1°, we find a strong relation between wetland emissions and the model resolution.
Tobias Küchler, Stefan Noël, Heinrich Bovensmann, John Philip Burrows, Thomas Wagner, Christian Borger, Tobias Borsdorff, and Andreas Schneider
Atmos. Meas. Tech., 15, 297–320, https://doi.org/10.5194/amt-15-297-2022, https://doi.org/10.5194/amt-15-297-2022, 2022
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We applied the air-mass-corrected differential optical absorption spectroscopy (AMC-DOAS) method to derive total column water vapour (TCWV) from Sentinel-5P measurements and compared it to independent data sets. The correlation coefficients of typically more than 0.9 and the small deviations up to 2.5 kg m−2 reveal good agreement between our data product and other TCWV data sets. In particular for the different Sentinel-5P water vapour products, the deviations are around 1 kg m−2.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
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We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Qiansi Tu, Frank Hase, Matthias Schneider, Omaira García, Thomas Blumenstock, Tobias Borsdorff, Matthias Frey, Farahnaz Khosrawi, Alba Lorente, Carlos Alberti, Juan J. Bustos, André Butz, Virgilio Carreño, Emilio Cuevas, Roger Curcoll, Christopher J. Diekmann, Darko Dubravica, Benjamin Ertl, Carme Estruch, Sergio Fabián León-Luis, Carlos Marrero, Josep-Anton Morgui, Ramón Ramos, Christian Scharun, Carsten Schneider, Eliezer Sepúlveda, Carlos Toledano, and Carlos Torres
Atmos. Chem. Phys., 22, 295–317, https://doi.org/10.5194/acp-22-295-2022, https://doi.org/10.5194/acp-22-295-2022, 2022
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We use different methane ground- and space-based remote sensing data sets for investigating the emission strength of three waste disposal sites close to Madrid. We present a method that uses wind-assigned anomalies for deriving emission strengths from satellite data and estimate their uncertainty to 9–14 %. The emission strengths estimated from the remote sensing data sets are significantly larger than the values published in the official register.
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, and Robert McLaren
Atmos. Chem. Phys., 21, 18101–18121, https://doi.org/10.5194/acp-21-18101-2021, https://doi.org/10.5194/acp-21-18101-2021, 2021
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We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada and lower natural emissions from Boreal wetlands.
Tyler Wizenberg, Kimberly Strong, Kaley Walker, Erik Lutsch, Tobias Borsdorff, and Jochen Landgraf
Atmos. Meas. Tech., 14, 7707–7728, https://doi.org/10.5194/amt-14-7707-2021, https://doi.org/10.5194/amt-14-7707-2021, 2021
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CO is an important atmospheric gas that influences both air quality and the climate. Here, we compare CO measurements from TROPOMI with those from ACE-FTS and an Arctic ground-based FTS at Eureka, Nunavut, to further characterize the accuracy of TROPOMI measurements. CO columns from the instruments agree well but show larger differences at high latitudes. Despite this, the results fall within the TROPOMI accuracy target, indicating good data quality at high latitudes.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
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Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Vilma Kangasaho, Aki Tsuruta, Leif Backman, Pyry Mäkinen, Sander Houweling, Arjo Segers, Maarten Krol, Ed Dlugokencky, Sylvia Michel, James White, and Tuula Aalto
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-843, https://doi.org/10.5194/acp-2021-843, 2021
Revised manuscript not accepted
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Understanding the composition of carbon isotopes can help to better understand the changes in methane budgets. This study investigates how methane sources affect the seasonal cycle of the methane carbon-13 isotope during 2000–2012 using an atmospheric transport model. We found that emissions from both anthropogenic and natural sources contribute. The findings raise a need to revise the magnitudes, proportion, and seasonal cycles of anthropogenic sources and northern wetland emissions.
Mahesh Kumar Sha, Bavo Langerock, Jean-François L. Blavier, Thomas Blumenstock, Tobias Borsdorff, Matthias Buschmann, Angelika Dehn, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Michel Grutter, James W. Hannigan, Frank Hase, Pauli Heikkinen, Christian Hermans, Laura T. Iraci, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Jochen Landgraf, Alba Lorente, Emmanuel Mahieu, Maria V. Makarova, Johan Mellqvist, Jean-Marc Metzger, Isamu Morino, Tomoo Nagahama, Justus Notholt, Hirofumi Ohyama, Ivan Ortega, Mathias Palm, Christof Petri, David F. Pollard, Markus Rettinger, John Robinson, Sébastien Roche, Coleen M. Roehl, Amelie N. Röhling, Constantina Rousogenous, Matthias Schneider, Kei Shiomi, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Osamu Uchino, Voltaire A. Velazco, Corinne Vigouroux, Mihalis Vrekoussis, Pucai Wang, Thorsten Warneke, Tyler Wizenberg, Debra Wunch, Shoma Yamanouchi, Yang Yang, and Minqiang Zhou
Atmos. Meas. Tech., 14, 6249–6304, https://doi.org/10.5194/amt-14-6249-2021, https://doi.org/10.5194/amt-14-6249-2021, 2021
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This paper presents, for the first time, Sentinel-5 Precursor methane and carbon monoxide validation results covering a period from November 2017 to September 2020. For this study, we used global TCCON and NDACC-IRWG network data covering a wide range of atmospheric and surface conditions across different terrains. We also show the influence of a priori alignment, smoothing uncertainties and the sensitivity of the validation results towards the application of advanced co-location criteria.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
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The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
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We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Tia R. Scarpelli, Melissa P. Sulprizio, Yuzhong Zhang, and Chris H. Rycroft
Atmos. Meas. Tech., 14, 5521–5534, https://doi.org/10.5194/amt-14-5521-2021, https://doi.org/10.5194/amt-14-5521-2021, 2021
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Analytical inversions of satellite observations of atmospheric composition can improve emissions estimates and quantify errors but are computationally expensive at high resolutions. We propose two methods to decrease this cost. The methods reproduce a high-resolution inversion at a quarter of the cost. The reduced-dimension method creates a multiscale grid. The reduced-rank method solves the inversion where information content is highest.
David R. Lyon, Benjamin Hmiel, Ritesh Gautam, Mark Omara, Katherine A. Roberts, Zachary R. Barkley, Kenneth J. Davis, Natasha L. Miles, Vanessa C. Monteiro, Scott J. Richardson, Stephen Conley, Mackenzie L. Smith, Daniel J. Jacob, Lu Shen, Daniel J. Varon, Aijun Deng, Xander Rudelis, Nikhil Sharma, Kyle T. Story, Adam R. Brandt, Mary Kang, Eric A. Kort, Anthony J. Marchese, and Steven P. Hamburg
Atmos. Chem. Phys., 21, 6605–6626, https://doi.org/10.5194/acp-21-6605-2021, https://doi.org/10.5194/acp-21-6605-2021, 2021
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The Permian Basin (USA) is the world’s largest oil field. We use tower- and aircraft-based approaches to measure how methane emissions in the Permian Basin changed throughout 2020. In early 2020, 3.3 % of the region’s gas was emitted; then in spring 2020, the loss rate temporarily dropped to 1.9 % as oil price crashed. We find this short-term reduction to be a result of reduced well development, less gas flaring, and fewer abnormal events despite minimal reductions in oil and gas production.
Daniel J. Varon, Dylan Jervis, Jason McKeever, Ian Spence, David Gains, and Daniel J. Jacob
Atmos. Meas. Tech., 14, 2771–2785, https://doi.org/10.5194/amt-14-2771-2021, https://doi.org/10.5194/amt-14-2771-2021, 2021
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Satellites can detect methane emissions by measuring sunlight reflected from the Earth's surface and atmosphere. Here we show that the European Space Agency's Sentinel-2 twin satellites can be used to monitor anomalously large methane point sources around the world, with global coverage every 2–5 days and 20 m spatial resolution. We demonstrate this previously unreported capability through high-frequency Sentinel-2 monitoring of two strong methane point sources in Algeria and Turkmenistan.
Jérôme Barré, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, Antje Inness, Alba Lorente, Joe McNorton, Vincent-Henri Peuch, Gabor Radnoti, and Roberto Ribas
Atmos. Chem. Phys., 21, 5117–5136, https://doi.org/10.5194/acp-21-5117-2021, https://doi.org/10.5194/acp-21-5117-2021, 2021
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This study presents a new approach to the systematic global detection of anomalous local CH4 concentration anomalies caused by rapid changes in anthropogenic emission levels. The approach utilises both satellite measurements and model simulations, and applies novel data analysis techniques (such as filtering and classification) to automatically detect anomalous emissions from point sources and small areas, such as oil and gas drilling sites, pipelines and facility leaks.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
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We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, and Robert J. Parker
Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, https://doi.org/10.5194/acp-21-4339-2021, 2021
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We use 2010–2015 GOSAT satellite observations of atmospheric methane over North America in a high-resolution inversion to estimate methane emissions. We find general consistency with the gridded EPA inventory but higher oil and gas production emissions, with oil production emissions twice as large as in the latest EPA Greenhouse Gas Inventory. We find lower wetland emissions than predicted by WetCHARTs and a small increasing trend in the eastern US, apparently related to unconventional oil/gas.
Dylan Jervis, Jason McKeever, Berke O. A. Durak, James J. Sloan, David Gains, Daniel J. Varon, Antoine Ramier, Mathias Strupler, and Ewan Tarrant
Atmos. Meas. Tech., 14, 2127–2140, https://doi.org/10.5194/amt-14-2127-2021, https://doi.org/10.5194/amt-14-2127-2021, 2021
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We describe how the GHGSat-D demonstration satellite is designed and operated in order to measure greenhouse gas emissions from different types of industrial facilities. The distinguishing features of GHGSat-D, or
Claire, are its compact size (< 15 kg) and high spatial resolution (< 50 m). We give a mathematical model of the instrument and describe the techniques used to infer a methane concentration from a measurement of the sunlight that has reflected off the Earth's surface.
Michael Buchwitz, Maximilian Reuter, Stefan Noël, Klaus Bramstedt, Oliver Schneising, Michael Hilker, Blanca Fuentes Andrade, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hartmut Boesch, Lianghai Wu, Jochen Landgraf, Ilse Aben, Christian Retscher, Christopher W. O'Dell, and David Crisp
Atmos. Meas. Tech., 14, 2141–2166, https://doi.org/10.5194/amt-14-2141-2021, https://doi.org/10.5194/amt-14-2141-2021, 2021
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The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We have used a small ensemble of satellite retrievals of column-averaged CO2 (XCO2) to find out if a regional-scale reduction of atmospheric CO2 can be detected from space. We focus on East China and show that it is challenging to reliably detect and to accurately quantify the emission reduction, which only results in regional XCO2 reductions of about 0.1–0.2 ppm.
Qiansi Tu, Frank Hase, Thomas Blumenstock, Matthias Schneider, Andreas Schneider, Rigel Kivi, Pauli Heikkinen, Benjamin Ertl, Christopher Diekmann, Farahnaz Khosrawi, Michael Sommer, Tobias Borsdorff, and Uwe Raffalski
Atmos. Meas. Tech., 14, 1993–2011, https://doi.org/10.5194/amt-14-1993-2021, https://doi.org/10.5194/amt-14-1993-2021, 2021
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We compare column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from the COllaborative Carbon Column Observing Network (COCCON) with two co-located ground-based spectrometers as references at two boreal sites. Our study supports the assumption that COCCON also delivers a well-characterized XH2O data product. This is the first published study applying COCCON for MUSICA IASI and TROPOMI validation.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
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We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
Marvin Knapp, Ralph Kleinschek, Frank Hase, Anna Agustí-Panareda, Antje Inness, Jérôme Barré, Jochen Landgraf, Tobias Borsdorff, Stefan Kinne, and André Butz
Earth Syst. Sci. Data, 13, 199–211, https://doi.org/10.5194/essd-13-199-2021, https://doi.org/10.5194/essd-13-199-2021, 2021
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We developed a shipborne variant of a remote sensing spectrometer for direct sunlight measurements of column-averaged atmospheric mixing ratios of carbon dioxide, methane, and carbon monoxide. The instrument was deployed on the research vessel Sonne during a longitudinal transect over the Pacific during June 2019. The campaign yielded more than 32 000 observations which compare excellently to atmospheric composition data from a state-of-the-art model (CAMS) and satellite observations (TROPOMI).
Alba Lorente, Tobias Borsdorff, Andre Butz, Otto Hasekamp, Joost aan de Brugh, Andreas Schneider, Lianghai Wu, Frank Hase, Rigel Kivi, Debra Wunch, David F. Pollard, Kei Shiomi, Nicholas M. Deutscher, Voltaire A. Velazco, Coleen M. Roehl, Paul O. Wennberg, Thorsten Warneke, and Jochen Landgraf
Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, https://doi.org/10.5194/amt-14-665-2021, 2021
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TROPOMI aboard Sentinel-5P satellite provides methane (CH4) measurements with exceptional temporal and spatial resolution. The study describes a series of improvements developed to retrieve CH4 from TROPOMI. The updated CH4 product features (among others) a more accurate a posteriori correction derived independently of any reference data. The validation of the improved data product shows good agreement with ground-based and satellite measurements, which highlights the quality of the TROPOMI CH4.
Sudhanshu Pandey, Sander Houweling, Alba Lorente, Tobias Borsdorff, Maria Tsivlidou, A. Anthony Bloom, Benjamin Poulter, Zhen Zhang, and Ilse Aben
Biogeosciences, 18, 557–572, https://doi.org/10.5194/bg-18-557-2021, https://doi.org/10.5194/bg-18-557-2021, 2021
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We use atmospheric methane observations from the novel TROPOspheric Monitoring Instrument (TROPOMI; Sentinel-5p) to estimate methane emissions from South Sudan's wetlands. Our emission estimates are an order of magnitude larger than the estimate of process-based wetland models. We find that this underestimation by the models is likely due to their misrepresentation of the wetlands' inundation extent and temperature dependences.
Ivar R. van der Velde, Guido R. van der Werf, Sander Houweling, Henk J. Eskes, J. Pepijn Veefkind, Tobias Borsdorff, and Ilse Aben
Atmos. Chem. Phys., 21, 597–616, https://doi.org/10.5194/acp-21-597-2021, https://doi.org/10.5194/acp-21-597-2021, 2021
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This paper compares the relative atmospheric enhancements of CO and NO2 measured by the space-based instrument TROPOMI over different fire-prone ecosystems around the world. We find distinct spatial and temporal patterns in the ΔNO2 / ΔCO ratio that correspond to regional differences in combustion efficiency. This joint analysis provides a better understanding of regional-scale combustion characteristics and can help the fire modeling community to improve existing global emission inventories.
Tobias Borsdorff, Agustín García Reynoso, Gilberto Maldonado, Bertha Mar-Morales, Wolfgang Stremme, Michel Grutter, and Jochen Landgraf
Atmos. Chem. Phys., 20, 15761–15774, https://doi.org/10.5194/acp-20-15761-2020, https://doi.org/10.5194/acp-20-15761-2020, 2020
Stelios Myriokefalitakis, Nikos Daskalakis, Angelos Gkouvousis, Andreas Hilboll, Twan van Noije, Jason E. Williams, Philippe Le Sager, Vincent Huijnen, Sander Houweling, Tommi Bergman, Johann Rasmus Nüß, Mihalis Vrekoussis, Maria Kanakidou, and Maarten C. Krol
Geosci. Model Dev., 13, 5507–5548, https://doi.org/10.5194/gmd-13-5507-2020, https://doi.org/10.5194/gmd-13-5507-2020, 2020
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This work documents and evaluates the detailed tropospheric gas-phase chemical mechanism MOGUNTIA in the three-dimensional chemistry transport model TM5-MP. The Rosenbrock solver, as generated by the KPP software, is implemented in the chemistry code, which can successfully replace the classical Euler backward integration method. The MOGUNTIA scheme satisfactorily simulates a large suite of oxygenated volatile organic compounds (VOCs) that are observed in the atmosphere at significant levels.
Sara Martínez-Alonso, Merritt Deeter, Helen Worden, Tobias Borsdorff, Ilse Aben, Róisin Commane, Bruce Daube, Gene Francis, Maya George, Jochen Landgraf, Debbie Mao, Kathryn McKain, and Steven Wofsy
Atmos. Meas. Tech., 13, 4841–4864, https://doi.org/10.5194/amt-13-4841-2020, https://doi.org/10.5194/amt-13-4841-2020, 2020
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CO is of great importance in climate and air quality studies. To understand newly available TROPOMI data in the frame of the global CO record, we compared those to satellite (MOPITT) and airborne (ATom) CO datasets. The MOPITT dataset is the longest to date (2000–present) and is well-characterized. We used ATom to validate cloudy TROPOMI data over oceans and investigate TROPOMI's vertical sensitivity to CO. Our results show that TROPOMI CO data are in excellent agreement with the other datasets.
Qiansi Tu, Frank Hase, Thomas Blumenstock, Rigel Kivi, Pauli Heikkinen, Mahesh Kumar Sha, Uwe Raffalski, Jochen Landgraf, Alba Lorente, Tobias Borsdorff, Huilin Chen, Florian Dietrich, and Jia Chen
Atmos. Meas. Tech., 13, 4751–4771, https://doi.org/10.5194/amt-13-4751-2020, https://doi.org/10.5194/amt-13-4751-2020, 2020
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Two COCCON instruments are used to observe multiyear greenhouse gases in boreal areas and are compared with the CAMS analysis and S5P satellite data. These three datasets predict greenhouse gas gradients with reasonable agreement. The results indicate that the COCCON instrument has the capability of measuring gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gases.
Srijana Lama, Sander Houweling, K. Folkert Boersma, Henk Eskes, Ilse Aben, Hugo A. C. Denier van der Gon, Maarten C. Krol, Han Dolman, Tobias Borsdorff, and Alba Lorente
Atmos. Chem. Phys., 20, 10295–10310, https://doi.org/10.5194/acp-20-10295-2020, https://doi.org/10.5194/acp-20-10295-2020, 2020
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Rapid urbanization has increased the consumption of fossil fuel, contributing the degradation of urban air quality. Burning efficiency is a major factor determining the impact of fuel burning on the environment. We quantify the burning efficiency of fossil fuel use over six megacities using satellite remote sensing data. City governance can use these results to understand air pollution scenarios and to formulate effective air pollution control strategies.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Steven Compernolle, Tijl Verhoelst, Gaia Pinardi, José Granville, Daan Hubert, Arno Keppens, Sander Niemeijer, Bruno Rino, Alkis Bais, Steffen Beirle, Folkert Boersma, John P. Burrows, Isabelle De Smedt, Henk Eskes, Florence Goutail, François Hendrick, Alba Lorente, Andrea Pazmino, Ankie Piters, Enno Peters, Jean-Pierre Pommereau, Julia Remmers, Andreas Richter, Jos van Geffen, Michel Van Roozendael, Thomas Wagner, and Jean-Christopher Lambert
Atmos. Chem. Phys., 20, 8017–8045, https://doi.org/10.5194/acp-20-8017-2020, https://doi.org/10.5194/acp-20-8017-2020, 2020
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Tropospheric and stratospheric NO2 columns from the OMI QA4ECV NO2 satellite product are validated by comparison with ground-based measurements at 11 sites. The OMI stratospheric column has a small negative bias, and the OMI tropospheric column has a stronger negative bias relative to the ground-based data. Discrepancies are attributed to comparison errors (e.g. difference in horizontal smoothing) and measurement errors (e.g. clouds, aerosols, vertical smoothing and a priori profile assumptions).
Peter H. Zimmermann, Carl A. M. Brenninkmeijer, Andrea Pozzer, Patrick Jöckel, Franziska Winterstein, Andreas Zahn, Sander Houweling, and Jos Lelieveld
Atmos. Chem. Phys., 20, 5787–5809, https://doi.org/10.5194/acp-20-5787-2020, https://doi.org/10.5194/acp-20-5787-2020, 2020
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The atmospheric abundance of the greenhouse gas methane is determined by interacting emission sources and sinks in a dynamic global environment. In this study, its global budget from 1997 to 2016 is simulated with a general circulation model using emission estimates of 11 source categories. The model results are evaluated against 17 ground station and 320 intercontinental flight observation series. Deviations are used to re-scale the emission quantities with the aim of matching observations.
Tia R. Scarpelli, Daniel J. Jacob, Joannes D. Maasakkers, Melissa P. Sulprizio, Jian-Xiong Sheng, Kelly Rose, Lucy Romeo, John R. Worden, and Greet Janssens-Maenhout
Earth Syst. Sci. Data, 12, 563–575, https://doi.org/10.5194/essd-12-563-2020, https://doi.org/10.5194/essd-12-563-2020, 2020
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Methane, a potent greenhouse gas, is emitted through the exploitation of oil, gas, and coal resources, and many efforts to reduce emissions have targeted these sources. We have created a global inventory of oil, gas, and coal methane emissions based on country reporting to the United Nations. The inventory can be used along with satellite observations of methane to better understand the contribution of these sources to global emissions and to identify potential biases in emissions reporting.
Maximilian Reuter, Michael Buchwitz, Oliver Schneising, Stefan Noël, Heinrich Bovensmann, John P. Burrows, Hartmut Boesch, Antonio Di Noia, Jasdeep Anand, Robert J. Parker, Peter Somkuti, Lianghai Wu, Otto P. Hasekamp, Ilse Aben, Akihiko Kuze, Hiroshi Suto, Kei Shiomi, Yukio Yoshida, Isamu Morino, David Crisp, Christopher W. O'Dell, Justus Notholt, Christof Petri, Thorsten Warneke, Voltaire A. Velazco, Nicholas M. Deutscher, David W. T. Griffith, Rigel Kivi, David F. Pollard, Frank Hase, Ralf Sussmann, Yao V. Té, Kimberly Strong, Sébastien Roche, Mahesh K. Sha, Martine De Mazière, Dietrich G. Feist, Laura T. Iraci, Coleen M. Roehl, Christian Retscher, and Dinand Schepers
Atmos. Meas. Tech., 13, 789–819, https://doi.org/10.5194/amt-13-789-2020, https://doi.org/10.5194/amt-13-789-2020, 2020
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We present new satellite-derived data sets of atmospheric carbon dioxide (CO2) and methane (CH4). The data products are column-averaged dry-air mole fractions of CO2 and CH4, denoted XCO2 and XCH4. The products cover the years 2003–2018 and are merged Level 2 (satellite footprints) and merged Level 3 (gridded at monthly time and 5° x 5° spatial resolution) products obtained from combining several individual sensor products. We present the merging algorithms and product validation results.
Andreas Schneider, Tobias Borsdorff, Joost aan de Brugh, Franziska Aemisegger, Dietrich G. Feist, Rigel Kivi, Frank Hase, Matthias Schneider, and Jochen Landgraf
Atmos. Meas. Tech., 13, 85–100, https://doi.org/10.5194/amt-13-85-2020, https://doi.org/10.5194/amt-13-85-2020, 2020
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This paper presents a new H2O/HDO data set from TROPOMI short-wave infrared measurements. It is validated against recent ground-based FTIR measurements from the TCCON network. A bias in TCCON HDO (which is not verified) is corrected by fitting a correction factor for the HDO column to match MUSICA δD for common observations. The use of the new TROPOMI data set is demonstrated using a case study of a blocking anticyclone over Europe in July 2018.
Tim A. van Kempen, Richard M. van Hees, Paul J. J. Tol, Ilse Aben, and Ruud W. M. Hoogeveen
Atmos. Meas. Tech., 12, 6827–6844, https://doi.org/10.5194/amt-12-6827-2019, https://doi.org/10.5194/amt-12-6827-2019, 2019
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This paper presents the TROPOMI-SWIR performance and health after a year of full operations. Using the on-going monitoring program, TROPOMI-SWIR is shown to be in excellent health and is performing as well as, if not better than, expected. With the exception of a tiny loss of detector pixels (less than 0.05 % over a full year), no components appear to be degrading. We show that TROPOMI-SWIR is expected to keep on providing excellent data for the full S5-P lifetime.
Oliver Schneising, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, John P. Burrows, Tobias Borsdorff, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Christian Hermans, Laura T. Iraci, Rigel Kivi, Jochen Landgraf, Isamu Morino, Justus Notholt, Christof Petri, David F. Pollard, Sébastien Roche, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Voltaire A. Velazco, Thorsten Warneke, and Debra Wunch
Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, https://doi.org/10.5194/amt-12-6771-2019, 2019
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We introduce an algorithm that is used to simultaneously derive the abundances of the important atmospheric constituents carbon monoxide and methane from the TROPOMI instrument onboard the Sentinel-5 Precursor satellite, which enables the determination of both gases with an unprecedented level of detail on a global scale. The quality of the resulting data sets is assessed and the first results are presented.
Lianghai Wu, Otto Hasekamp, Haili Hu, Joost aan de Brugh, Jochen Landgraf, Andre Butz, and Ilse Aben
Atmos. Meas. Tech., 12, 6049–6058, https://doi.org/10.5194/amt-12-6049-2019, https://doi.org/10.5194/amt-12-6049-2019, 2019
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We propose a one–band XCO2 retrieval technique which uses only the 2.06 µm band measurements from the Orbiting Carbon Observatory–2 (OCO–2) satellite. Compared to the current state–of–the–art three–band retrievals, XCO2 retrievals using only the 2.06 µm band have similar retrieval accuracy, precision, and data yield. For future missions it may be better to replace the O2 A band with measurements that have larger information content on aerosols, like a multi–angle polarimeter (MAP).
Daniel H. Cusworth, Daniel J. Jacob, Daniel J. Varon, Christopher Chan Miller, Xiong Liu, Kelly Chance, Andrew K. Thorpe, Riley M. Duren, Charles E. Miller, David R. Thompson, Christian Frankenberg, Luis Guanter, and Cynthia A. Randles
Atmos. Meas. Tech., 12, 5655–5668, https://doi.org/10.5194/amt-12-5655-2019, https://doi.org/10.5194/amt-12-5655-2019, 2019
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We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers scheduled for launch in 2019–2025. We perform methane retrievals on simulated scenes with varying surfaces and atmospheric methane concentrations. Our results suggest that imaging spectrometers in space could play a transformative role in the future for quantifying methane emissions from point sources on a global scale.
Tobias Borsdorff, Joost aan de Brugh, Andreas Schneider, Alba Lorente, Manfred Birk, Georg Wagner, Rigel Kivi, Frank Hase, Dietrich G. Feist, Ralf Sussmann, Markus Rettinger, Debra Wunch, Thorsten Warneke, and Jochen Landgraf
Atmos. Meas. Tech., 12, 5443–5455, https://doi.org/10.5194/amt-12-5443-2019, https://doi.org/10.5194/amt-12-5443-2019, 2019
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The study presents possible improvements of the TROPOMI CO dataset, which is a primary product of ESA's Sentinel-5P mission. We discuss the use of different molecular spectroscopic databases in the CO retrieval, the induced biases between TROPOMI CO and TCCON validation measurements, and the latitudinally dependent bias between TROPOMI CO and the CAMS-IFS model. Additionally, two methods for the stripe correction of single TROPOMI CO orbits are presented.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jian-Xiong Sheng, Yuzhong Zhang, Monica Hersher, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, Greet Janssens-Maenhout, and Robert J. Parker
Atmos. Chem. Phys., 19, 7859–7881, https://doi.org/10.5194/acp-19-7859-2019, https://doi.org/10.5194/acp-19-7859-2019, 2019
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We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane emissions and their trends, as well as the concentration and trend of tropospheric OH (hydroxyl radical, methane's main sink). We find overestimates of Chinese coal and Middle East oil/gas emissions in the prior estimate. The 2010–2015 growth in methane is attributed to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH is small in comparison.
Debra Wunch, Dylan B. A. Jones, Geoffrey C. Toon, Nicholas M. Deutscher, Frank Hase, Justus Notholt, Ralf Sussmann, Thorsten Warneke, Jeroen Kuenen, Hugo Denier van der Gon, Jenny A. Fisher, and Joannes D. Maasakkers
Atmos. Chem. Phys., 19, 3963–3980, https://doi.org/10.5194/acp-19-3963-2019, https://doi.org/10.5194/acp-19-3963-2019, 2019
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We used five atmospheric observatories in Europe measuring total column dry-air mole fractions of methane and carbon monoxide to infer methane emissions in the area between the observatories. We find that the methane emissions are overestimated by the state-of-the-art inventories, and that this is likely due, at least in part, to the inventory disaggregation. We find that there is significant uncertainty in the carbon monoxide inventories that requires further investigation.
Tobias Borsdorff, Joost aan de Brugh, Sudhanshu Pandey, Otto Hasekamp, Ilse Aben, Sander Houweling, and Jochen Landgraf
Atmos. Chem. Phys., 19, 3579–3588, https://doi.org/10.5194/acp-19-3579-2019, https://doi.org/10.5194/acp-19-3579-2019, 2019
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The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite provides carbon monoxide (CO) total column concentrations based on measurements in the 2.3 μm spectral range with a spatial resolution of 7 km x 7 km and daily global coverage. In this study, we analyzed local CO enhancements in an area around Iran from 1 November to 20 December 2017 using the WRF model and evaluated CO emissions from the cities of Tehran, Yerevan, Urmia, and Tabriz.
Iris N. Dekker, Sander Houweling, Sudhanshu Pandey, Maarten Krol, Thomas Röckmann, Tobias Borsdorff, Jochen Landgraf, and Ilse Aben
Atmos. Chem. Phys., 19, 3433–3445, https://doi.org/10.5194/acp-19-3433-2019, https://doi.org/10.5194/acp-19-3433-2019, 2019
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During November 2017, very high pollution levels were measured in the northern part of India. In this study, satellite (TROPOMI) data and model (WRF) data on carbon monoxide (CO) are studied to investigate the main sources of the CO pollution over the Indo-Gangetic Plain. We found that residential and commercial combustion was a much more important source of CO than the post-monsoon crop burning during this period. Meteorology was found important in the accumulation and ventilation of CO.
K. Folkert Boersma, Henk J. Eskes, Andreas Richter, Isabelle De Smedt, Alba Lorente, Steffen Beirle, Jos H. G. M. van Geffen, Marina Zara, Enno Peters, Michel Van Roozendael, Thomas Wagner, Joannes D. Maasakkers, Ronald J. van der A, Joanne Nightingale, Anne De Rudder, Hitoshi Irie, Gaia Pinardi, Jean-Christopher Lambert, and Steven C. Compernolle
Atmos. Meas. Tech., 11, 6651–6678, https://doi.org/10.5194/amt-11-6651-2018, https://doi.org/10.5194/amt-11-6651-2018, 2018
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This paper describes a new, improved data record of 22+ years of coherent nitrogen dioxide (NO2) pollution measurements from different satellite instruments. Our work helps to ensure that climate data are of sufficient quality to draw reliable conclusions and shape decisions. It shows how dedicated intercomparisons of retrieval sub-steps have led to improved NO2 measurements from the GOME, SCIAMACHY, GOME-2(A), and OMI sensors, and how quality assurance of the new data product is achieved.
Michael Buchwitz, Maximilian Reuter, Oliver Schneising, Stefan Noël, Bettina Gier, Heinrich Bovensmann, John P. Burrows, Hartmut Boesch, Jasdeep Anand, Robert J. Parker, Peter Somkuti, Rob G. Detmers, Otto P. Hasekamp, Ilse Aben, André Butz, Akihiko Kuze, Hiroshi Suto, Yukio Yoshida, David Crisp, and Christopher O'Dell
Atmos. Chem. Phys., 18, 17355–17370, https://doi.org/10.5194/acp-18-17355-2018, https://doi.org/10.5194/acp-18-17355-2018, 2018
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We present a new satellite data set of column-averaged mixing ratios of carbon dioxide (CO2), which covers the time period 2003 to 2016. We used this data set to compute annual mean atmospheric CO2 growth rates. We show that the growth rate is highest during 2015 and 2016 despite nearly constant CO2 emissions from fossil fuel burning in recent years. The high growth rates are attributed to year 2015-2016 El Nino episodes. We present correlations with fossil fuel emissions and ENSO indices.
Jian-Xiong Sheng, Daniel J. Jacob, Joannes D. Maasakkers, Yuzhong Zhang, and Melissa P. Sulprizio
Atmos. Meas. Tech., 11, 6379–6388, https://doi.org/10.5194/amt-11-6379-2018, https://doi.org/10.5194/amt-11-6379-2018, 2018
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We conduct Observing System Simulation Experiments to compare the ability of future satellite measurements of atmospheric methane columns for constraining methane emissions at the 25 km scale. We find that the geostationary instruments can do much better than TROPOMI and are less sensitive to cloud cover. GeoCARB observing twice a day would provide 70 % of the information from the nominal GEO-CAPE mission considered by NASA in response to the Decadal Survey of the US National Research Council.
Daniel H. Cusworth, Daniel J. Jacob, Jian-Xiong Sheng, Joshua Benmergui, Alexander J. Turner, Jeremy Brandman, Laurent White, and Cynthia A. Randles
Atmos. Chem. Phys., 18, 16885–16896, https://doi.org/10.5194/acp-18-16885-2018, https://doi.org/10.5194/acp-18-16885-2018, 2018
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Methane emissions from oil/gas fields originate from a large number of small and densely clustered point sources. We examine the potential of recently launched or planned satellites to locate these high-mode emitters through measurements of atmospheric methane. We find that the recently launched TROPOMI and the planned GeoCARB instruments are successful at locating high-emitting sources for fields of 20-50 emitters within the 50 × 50 km2 geographic domain but are unsuccessful for denser fields.
Yuzhong Zhang, Daniel J. Jacob, Joannes D. Maasakkers, Melissa P. Sulprizio, Jian-Xiong Sheng, Ritesh Gautam, and John Worden
Atmos. Chem. Phys., 18, 15959–15973, https://doi.org/10.5194/acp-18-15959-2018, https://doi.org/10.5194/acp-18-15959-2018, 2018
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We assess the potential of using satellite observations of atmospheric methane to monitor global mean tropospheric OH concentration, a key parameter for the oxidizing power of the atmosphere.
Daniel J. Varon, Daniel J. Jacob, Jason McKeever, Dylan Jervis, Berke O. A. Durak, Yan Xia, and Yi Huang
Atmos. Meas. Tech., 11, 5673–5686, https://doi.org/10.5194/amt-11-5673-2018, https://doi.org/10.5194/amt-11-5673-2018, 2018
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Methane is a powerful greenhouse gas emitted from numerous human activities. Space-based observation of point sources would be a cost-effective monitoring solution, but the resolution of most current and planned methane-observing satellites is too coarse to resolve individual emitters. We simulate fine-resolution (50 m) satellite observations of methane plumes as would be measured by GHGSat (to be launched in 2019) and show that such data can usefully quantify large methane point sources.
Tobias Borsdorff, Joost aan de Brugh, Haili Hu, Otto Hasekamp, Ralf Sussmann, Markus Rettinger, Frank Hase, Jochen Gross, Matthias Schneider, Omaira Garcia, Wolfgang Stremme, Michel Grutter, Dietrich G. Feist, Sabrina G. Arnold, Martine De Mazière, Mahesh Kumar Sha, David F. Pollard, Matthäus Kiel, Coleen Roehl, Paul O. Wennberg, Geoffrey C. Toon, and Jochen Landgraf
Atmos. Meas. Tech., 11, 5507–5518, https://doi.org/10.5194/amt-11-5507-2018, https://doi.org/10.5194/amt-11-5507-2018, 2018
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On 13 October 2017, the S5-P satellite was launched with TROPOMI as its only payload. One of the primary products is atmospheric CO observed with daily global coverage and spatial resolution of 7 × 7 km2. The new dataset allows the sensing of CO enhancements above cities and industrial areas and can track pollution transport from biomass burning regions. Through validation with ground-based TCCON measurements we show that the CO data product is already well within the mission requirement.
Jian-Xiong Sheng, Daniel J. Jacob, Alexander J. Turner, Joannes D. Maasakkers, Joshua Benmergui, A. Anthony Bloom, Claudia Arndt, Ritesh Gautam, Daniel Zavala-Araiza, Hartmut Boesch, and Robert J. Parker
Atmos. Chem. Phys., 18, 12257–12267, https://doi.org/10.5194/acp-18-12257-2018, https://doi.org/10.5194/acp-18-12257-2018, 2018
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Analysis of 7 years (2010–2016) of GOSAT methane trends over Canada, the contiguous US, and Mexico suggests that US methane emissions increased by 2.5 ± 1.4 % a−1 over the 7-year period, with contributions from both oil–gas systems and livestock in the Midwest. Mexican emissions show a decrease that can be attributed to a decreasing cattle population. Canadian emissions show year-to-year variability driven by wetland emissions and correlated with wetland areal extent.
Paul J. J. Tol, Tim A. van Kempen, Richard M. van Hees, Matthijs Krijger, Sidney Cadot, Ralph Snel, Stefan T. Persijn, Ilse Aben, and Ruud W. M. Hoogeveen
Atmos. Meas. Tech., 11, 4493–4507, https://doi.org/10.5194/amt-11-4493-2018, https://doi.org/10.5194/amt-11-4493-2018, 2018
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The shortwave infrared (SWIR) spectrometer module of the Tropospheric Monitoring Instrument (TROPOMI) is used to measure atmospheric CO and methane columns from space. A method has been developed and applied in an on-ground calibration campaign to characterize stray light in detail. An algorithm was then devised to correct in-flight observations in near-real time, reducing the stray-light signal sufficiently for accurate gas-column retrievals.
Alba Lorente, K. Folkert Boersma, Piet Stammes, L. Gijsbert Tilstra, Andreas Richter, Huan Yu, Said Kharbouche, and Jan-Peter Muller
Atmos. Meas. Tech., 11, 4509–4529, https://doi.org/10.5194/amt-11-4509-2018, https://doi.org/10.5194/amt-11-4509-2018, 2018
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Light reflected by Earth’s surface is different in each direction: it appears brighter or darker in certain viewing directions. Currently this effect is not accounted for in satellite retrievals; thus surface reflectance climatologies and cloud fractions show an east-west bias across orbits (GOME2,OMI). The effect for NO2 measurements in partly cloudy scenes is substantial. We recommend that this effect in UV/Vis sensors coherently accounted for, and will be especially beneficial for TROPOMI.
Richard M. van Hees, Paul J. J. Tol, Sidney Cadot, Matthijs Krijger, Stefan T. Persijn, Tim A. van Kempen, Ralph Snel, Ilse Aben, and Ruud W. M. Hoogeveen
Atmos. Meas. Tech., 11, 3917–3933, https://doi.org/10.5194/amt-11-3917-2018, https://doi.org/10.5194/amt-11-3917-2018, 2018
Andreas Schneider, Tobias Borsdorff, Joost aan de Brugh, Haili Hu, and Jochen Landgraf
Atmos. Meas. Tech., 11, 3339–3350, https://doi.org/10.5194/amt-11-3339-2018, https://doi.org/10.5194/amt-11-3339-2018, 2018
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A new data set of vertical column densities of the water vapour isotopologues H2O and HDO retrieved from short-wave infrared measurements (2339 nm to 2383 nm) by the SCIAMACHY satellite instrument for the whole of the mission period from 2003 to 2012 is presented. The data are validated against ground-based Fourier transform infrared measurements. High-altitude stations observe different air columns; thus in this case collocated scenes with clouds around station height are needed for agreement.
Lianghai Wu, Otto Hasekamp, Haili Hu, Jochen Landgraf, Andre Butz, Joost aan de Brugh, Ilse Aben, Dave F. Pollard, David W. T. Griffith, Dietrich G. Feist, Dmitry Koshelev, Frank Hase, Geoffrey C. Toon, Hirofumi Ohyama, Isamu Morino, Justus Notholt, Kei Shiomi, Laura Iraci, Matthias Schneider, Martine de Mazière, Ralf Sussmann, Rigel Kivi, Thorsten Warneke, Tae-Young Goo, and Yao Té
Atmos. Meas. Tech., 11, 3111–3130, https://doi.org/10.5194/amt-11-3111-2018, https://doi.org/10.5194/amt-11-3111-2018, 2018
Sandy P. Harrison, Patrick J. Bartlein, Victor Brovkin, Sander Houweling, Silvia Kloster, and I. Colin Prentice
Earth Syst. Dynam., 9, 663–677, https://doi.org/10.5194/esd-9-663-2018, https://doi.org/10.5194/esd-9-663-2018, 2018
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Temperature affects fire occurrence and severity. Warming will increase fire-related carbon emissions and thus atmospheric CO2. The size of this feedback is not known. We use charcoal records to estimate pre-industrial fire emissions and a simple land–biosphere model to quantify the feedback. We infer a feedback strength of 5.6 3.2 ppm CO2 per degree of warming and a gain of 0.09 ± 0.05 for a climate sensitivity of 2.8 K. Thus, fire feedback is a large part of the climate–carbon-cycle feedback.
Jian-Xiong Sheng, Daniel J. Jacob, Alexander J. Turner, Joannes D. Maasakkers, Melissa P. Sulprizio, A. Anthony Bloom, Arlyn E. Andrews, and Debra Wunch
Atmos. Chem. Phys., 18, 6483–6491, https://doi.org/10.5194/acp-18-6483-2018, https://doi.org/10.5194/acp-18-6483-2018, 2018
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We use observations of boundary layer methane from the SEAC4RS aircraft campaign over the Southeast US to estimate methane emissions in that region. Our results suggest that the EPA inventory is regionally unbiased but there are large local biases, suggesting variable emission factors. Our results also suggest that the choice of landcover map is the dominant source of error for wetland emission estimates.
Tobias Borsdorff, Josip Andrasec, Joost aan de Brugh, Haili Hu, Ilse Aben, and Jochen Landgraf
Atmos. Meas. Tech., 11, 2553–2565, https://doi.org/10.5194/amt-11-2553-2018, https://doi.org/10.5194/amt-11-2553-2018, 2018
Iris N. Dekker, Sander Houweling, Ilse Aben, Thomas Röckmann, Maarten Krol, Sara Martínez-Alonso, Merritt N. Deeter, and Helen M. Worden
Atmos. Chem. Phys., 17, 14675–14694, https://doi.org/10.5194/acp-17-14675-2017, https://doi.org/10.5194/acp-17-14675-2017, 2017
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This study estimates carbon monoxide emissions from the city of Madrid using MOPITT satellite data. There are two methods used and reviewed in this paper: a method that can only estimate a trend in the emission and a newly developed method that also includes model data from WRF to quantify the emissions. We find Madrid CO emissions to be lower by 48 % for 2002 and by 17 % for 2006 compared with the EdgarV4.2 emission inventory, but uncertainty (20 to 50 %) remains.
Zachary R. Barkley, Thomas Lauvaux, Kenneth J. Davis, Aijun Deng, Natasha L. Miles, Scott J. Richardson, Yanni Cao, Colm Sweeney, Anna Karion, MacKenzie Smith, Eric A. Kort, Stefan Schwietzke, Thomas Murphy, Guido Cervone, Douglas Martins, and Joannes D. Maasakkers
Atmos. Chem. Phys., 17, 13941–13966, https://doi.org/10.5194/acp-17-13941-2017, https://doi.org/10.5194/acp-17-13941-2017, 2017
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This study quantifies methane emissions from natural gas production in north-eastern Pennsylvania. Methane observations from 10 flights in spring 2015 are compared to model-projected values, and methane emissions from natural gas are adjusted within the model to create the best match between the two data sets. This study find methane emissions from natural gas production to be low and may be indicative of characteristics of the basin that make sources from north-eastern Pennsylvania unique.
Daniel H. Cusworth, Loretta J. Mickley, Eric M. Leibensperger, and Michael J. Iacono
Atmos. Chem. Phys., 17, 13559–13572, https://doi.org/10.5194/acp-17-13559-2017, https://doi.org/10.5194/acp-17-13559-2017, 2017
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Since 1990, light-scattering pollution known as aerosols have declined as a result of tightening US air quality regulations. Our study finds that US surface solar radiation has increased simultaneously. We establish a link between aerosols and radiation through physical and statistical models. We find the strongest relationship between aerosols, radiation, and climate at a site in the Midwest. Our work underscores the importance of regional pollution on climate in the US and abroad.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Tobias Borsdorff, Joost aan de Brugh, Haili Hu, Philippe Nédélec, Ilse Aben, and Jochen Landgraf
Atmos. Meas. Tech., 10, 1769–1782, https://doi.org/10.5194/amt-10-1769-2017, https://doi.org/10.5194/amt-10-1769-2017, 2017
Michael Buchwitz, Oliver Schneising, Maximilian Reuter, Jens Heymann, Sven Krautwurst, Heinrich Bovensmann, John P. Burrows, Hartmut Boesch, Robert J. Parker, Peter Somkuti, Rob G. Detmers, Otto P. Hasekamp, Ilse Aben, André Butz, Christian Frankenberg, and Alexander J. Turner
Atmos. Chem. Phys., 17, 5751–5774, https://doi.org/10.5194/acp-17-5751-2017, https://doi.org/10.5194/acp-17-5751-2017, 2017
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Methane is an important greenhouse gas and increasing atmospheric concentrations result in global warming. We present a simple method to derive annual methane emission estimates of methane hotspot areas from satellite data. We present results for four source areas. We found that our estimates are in good agreement with other studies/data sets for the Four Corners region in the USA and for Azerbaijan but we also found higher emissions for parts of California and Turkmenistan.
Aki Tsuruta, Tuula Aalto, Leif Backman, Janne Hakkarainen, Ingrid T. van der Laan-Luijkx, Maarten C. Krol, Renato Spahni, Sander Houweling, Marko Laine, Ed Dlugokencky, Angel J. Gomez-Pelaez, Marcel van der Schoot, Ray Langenfelds, Raymond Ellul, Jgor Arduini, Francesco Apadula, Christoph Gerbig, Dietrich G. Feist, Rigel Kivi, Yukio Yoshida, and Wouter Peters
Geosci. Model Dev., 10, 1261–1289, https://doi.org/10.5194/gmd-10-1261-2017, https://doi.org/10.5194/gmd-10-1261-2017, 2017
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In this study, we found that the average global methane emission for 2000–2012, estimated by the CTE-CH4 model, was 516±51 Tg CH4 yr-1, and the estimates for 2007–2012 were 4 % larger than for 2000–2006. The model estimates are sensitive to inputs and setups, but according to sensitivity tests the study suggests that the increase in atmospheric methane concentrations during 21st century was due to an increase in emissions from the 35S-EQ latitudinal bands.
Alba Lorente, K. Folkert Boersma, Huan Yu, Steffen Dörner, Andreas Hilboll, Andreas Richter, Mengyao Liu, Lok N. Lamsal, Michael Barkley, Isabelle De Smedt, Michel Van Roozendael, Yang Wang, Thomas Wagner, Steffen Beirle, Jin-Tai Lin, Nickolay Krotkov, Piet Stammes, Ping Wang, Henk J. Eskes, and Maarten Krol
Atmos. Meas. Tech., 10, 759–782, https://doi.org/10.5194/amt-10-759-2017, https://doi.org/10.5194/amt-10-759-2017, 2017
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Choices and assumptions made to represent the state of the atmosphere introduce an uncertainty of 42 % in the air mass factor calculation in trace gas satellite retrievals in polluted regions. The AMF strongly depends on the choice of a priori trace gas profile, surface albedo data set and the correction method to account for clouds and aerosols. We call for well-designed validation exercises focusing on situations when AMF structural uncertainty has the highest impact on satellite retrievals.
Sander Houweling, Peter Bergamaschi, Frederic Chevallier, Martin Heimann, Thomas Kaminski, Maarten Krol, Anna M. Michalak, and Prabir Patra
Atmos. Chem. Phys., 17, 235–256, https://doi.org/10.5194/acp-17-235-2017, https://doi.org/10.5194/acp-17-235-2017, 2017
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The aim of this paper is to present an overview of inverse modeling methods, developed over the years, for estimating the global sources and sinks of the greenhouse gas methane from atmospheric measurements. It provides insight into how techniques and estimates have evolved over time, what the remaining shortcomings are, new developments, and promising future directions.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Daniel J. Jacob, Alexander J. Turner, Joannes D. Maasakkers, Jianxiong Sheng, Kang Sun, Xiong Liu, Kelly Chance, Ilse Aben, Jason McKeever, and Christian Frankenberg
Atmos. Chem. Phys., 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, https://doi.org/10.5194/acp-16-14371-2016, 2016
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Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned to launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify methane emissions from the global scale down to the scale of point sources.
Haili Hu, Otto Hasekamp, André Butz, André Galli, Jochen Landgraf, Joost Aan de Brugh, Tobias Borsdorff, Remco Scheepmaker, and Ilse Aben
Atmos. Meas. Tech., 9, 5423–5440, https://doi.org/10.5194/amt-9-5423-2016, https://doi.org/10.5194/amt-9-5423-2016, 2016
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In 2017, the TROPOMI spectrometer will be launched on board the Sentinel 5 Precursor satellite. It will deliver, among other things, daily global measurements of methane as part of the Copernicus atmospheric services.
In this paper, we present the algorithm that is used for operational data processing of the methane product from TROPOMI measurements of the shortwave and near-infrared spectral range, and we discuss its performance using realistic simulated measurements.
Jochen Landgraf, Joost aan de Brugh, Remco Scheepmaker, Tobias Borsdorff, Haili Hu, Sander Houweling, Andre Butz, Ilse Aben, and Otto Hasekamp
Atmos. Meas. Tech., 9, 4955–4975, https://doi.org/10.5194/amt-9-4955-2016, https://doi.org/10.5194/amt-9-4955-2016, 2016
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In 2016, the Sentinel 5 Precursor mission will be launched, with the TROPOMI instrument as its single payload. It will deliver daily global measurements of carbon monoxide for air quality monitoring as part of the Copernicus atmospheric services. In this paper, we focus on the operational data processing of the CO product from TROPOMI measurements of the shortwave infrared spectral range, and we discuss the algorithm's maturity.
Andreas Ostler, Ralf Sussmann, Prabir K. Patra, Sander Houweling, Marko De Bruine, Gabriele P. Stiller, Florian J. Haenel, Johannes Plieninger, Philippe Bousquet, Yi Yin, Marielle Saunois, Kaley A. Walker, Nicholas M. Deutscher, David W. T. Griffith, Thomas Blumenstock, Frank Hase, Thorsten Warneke, Zhiting Wang, Rigel Kivi, and John Robinson
Atmos. Meas. Tech., 9, 4843–4859, https://doi.org/10.5194/amt-9-4843-2016, https://doi.org/10.5194/amt-9-4843-2016, 2016
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Our evaluation of column-averaged methane (XCH4) in models and TCCON reveals latitudinal biases between 0.4 % and 2.1 % originating from an inter-model spread in stratospheric CH4. Substituting model stratospheric CH4 fields by satellite data significantly reduces the large XCH4 bias observed for one model. For other models, showing only minor biases, the impact is ambiguous; i.e., the satellite uncertainty range hinders a more accurate model evaluation needed to improve inverse modeling.
Remco A. Scheepmaker, Joost aan de Brugh, Haili Hu, Tobias Borsdorff, Christian Frankenberg, Camille Risi, Otto Hasekamp, Ilse Aben, and Jochen Landgraf
Atmos. Meas. Tech., 9, 3921–3937, https://doi.org/10.5194/amt-9-3921-2016, https://doi.org/10.5194/amt-9-3921-2016, 2016
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We have developed an algorithm to measure HDO (heavy water) in the atmosphere using the TROPOMI satellite instrument, scheduled for launch in 2016. Giving an insight in the history of water vapour, these measurements will help to better understand the water cycle and its role in climate change. We use realistic measurement simulations to describe the performance of the algorithm, and show that TROPOMI will greatly improve and extend the HDO datasets from the previous SCIAMACHY and GOSAT missions.
Thomas Röckmann, Simon Eyer, Carina van der Veen, Maria E. Popa, Béla Tuzson, Guillaume Monteil, Sander Houweling, Eliza Harris, Dominik Brunner, Hubertus Fischer, Giulia Zazzeri, David Lowry, Euan G. Nisbet, Willi A. Brand, Jaroslav M. Necki, Lukas Emmenegger, and Joachim Mohn
Atmos. Chem. Phys., 16, 10469–10487, https://doi.org/10.5194/acp-16-10469-2016, https://doi.org/10.5194/acp-16-10469-2016, 2016
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A dual isotope ratio mass spectrometric system (IRMS) and a quantum cascade laser absorption spectroscopy (QCLAS)-based technique were deployed at the Cabauw experimental site for atmospheric research (CESAR) in the Netherlands and performed in situ, high-frequency (approx. hourly) measurements for a period of more than 5 months, yielding a combined dataset with more than 2500 measurements of both δ13C and δD.
Aki Tsuruta, Tuula Aalto, Leif Backman, Janne Hakkarainen, Ingrid T. van der Laan-Luijkx, Maarten C. Krol, Renato Spahni, Sander Houweling, Marko Laine, Marcel van der Schoot, Ray Langenfelds, Raymond Ellul, and Wouter Peters
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-181, https://doi.org/10.5194/gmd-2016-181, 2016
Revised manuscript has not been submitted
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In this study, we found that methane emission estimates, driven by the CTE-CH4 model, depend on model setups and inputs, especially for regional estimates. An optimal setup makes the estimates stable, but inputs, such as emission estimates from inventories, and observations, also play significant role. The results can be used for an extended analysis on relative contributions of methane emissions to atmospheric methane concentration changes in recent decades.
Dejian Fu, Kevin W. Bowman, Helen M. Worden, Vijay Natraj, John R. Worden, Shanshan Yu, Pepijn Veefkind, Ilse Aben, Jochen Landgraf, Larrabee Strow, and Yong Han
Atmos. Meas. Tech., 9, 2567–2579, https://doi.org/10.5194/amt-9-2567-2016, https://doi.org/10.5194/amt-9-2567-2016, 2016
Sudhanshu Pandey, Sander Houweling, Maarten Krol, Ilse Aben, Frédéric Chevallier, Edward J. Dlugokencky, Luciana V. Gatti, Emanuel Gloor, John B. Miller, Rob Detmers, Toshinobu Machida, and Thomas Röckmann
Atmos. Chem. Phys., 16, 5043–5062, https://doi.org/10.5194/acp-16-5043-2016, https://doi.org/10.5194/acp-16-5043-2016, 2016
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This study investigates the constraint provided by measurements of Xratio (XCH4/XCO2) from space on surface fluxes of CH4 and CO2. We apply the ratio inversion method described in Pandey et al. (2015) to Xratio retrievals from the GOSAT with the TM5-4DVAR inverse modeling system, to constrain the surface fluxes of CH4 and CO2 for 2009 and 2010. The results are compared to proxy CH4 inversions using model-derived-XCO2 mixing ratios from CarbonTracker and MACC.
T. Borsdorff, P. Tol, J. E. Williams, J. de Laat, J. aan de Brugh, P. Nédélec, I. Aben, and J. Landgraf
Atmos. Meas. Tech., 9, 227–248, https://doi.org/10.5194/amt-9-227-2016, https://doi.org/10.5194/amt-9-227-2016, 2016
A. Wassmann, T. Borsdorff, J. M. J. aan de Brugh, O. P. Hasekamp, I. Aben, and J. Landgraf
Atmos. Meas. Tech., 8, 4429–4451, https://doi.org/10.5194/amt-8-4429-2015, https://doi.org/10.5194/amt-8-4429-2015, 2015
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We present an extensive sensitivity study of retrieved total ozone columns from clear sky Global Ozone Monitoring Experiment 2 (GOME-2) measurements between 325 and 335nm which are corrected for instrument degradation. We address the choice of the scaling ozone profile, the choice of the radiative transfer solver, and the approximation of Earth's sphericity. Finally, we study the effect of instrument degradation on the retrieved total ozone columns for the first 4 years of the mission.
A. Babenhauserheide, S. Basu, S. Houweling, W. Peters, and A. Butz
Atmos. Chem. Phys., 15, 9747–9763, https://doi.org/10.5194/acp-15-9747-2015, https://doi.org/10.5194/acp-15-9747-2015, 2015
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We compare two different data assimilation systems for estimating sources and sinks of CO_2 from concentration measurements. The systems are CarbonTracker and TM5-4DVar, which have both been used in a number of scientific studies. We analyze the differences between both models as well as the sensitivity of the estimated sources and sinks to the observation coverage. The results provide a lower limit for the uncertainty of surface carbon fluxes with the current measurement network.
S. Pandey, S. Houweling, M. Krol, I. Aben, and T. Röckmann
Atmos. Chem. Phys., 15, 8615–8629, https://doi.org/10.5194/acp-15-8615-2015, https://doi.org/10.5194/acp-15-8615-2015, 2015
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This study attempts to determine the feasibility of a new assimilation method of satellite measurements of CH4 and CO2 for optimization of their surface fluxes in a synthetic environment. Instead of their absolute concentrations, we assimilate the ratios of their concentrations (CH4/CO2) in our inversion. Doing so helps us to reduce the effect of atmospheric scattering on the measurements in our system. However, assimilation of the ratios makes the inversion non-linear.
R. A. Scheepmaker, C. Frankenberg, N. M. Deutscher, M. Schneider, S. Barthlott, T. Blumenstock, O. E. Garcia, F. Hase, N. Jones, E. Mahieu, J. Notholt, V. Velazco, J. Landgraf, and I. Aben
Atmos. Meas. Tech., 8, 1799–1818, https://doi.org/10.5194/amt-8-1799-2015, https://doi.org/10.5194/amt-8-1799-2015, 2015
S. J. Sutanto, G. Hoffmann, R. A. Scheepmaker, J. Worden, S. Houweling, K. Yoshimura, I. Aben, and T. Röckmann
Atmos. Meas. Tech., 8, 999–1019, https://doi.org/10.5194/amt-8-999-2015, https://doi.org/10.5194/amt-8-999-2015, 2015
G. C. M. Vinken, K. F. Boersma, J. D. Maasakkers, M. Adon, and R. V. Martin
Atmos. Chem. Phys., 14, 10363–10381, https://doi.org/10.5194/acp-14-10363-2014, https://doi.org/10.5194/acp-14-10363-2014, 2014
S. Houweling, M. Krol, P. Bergamaschi, C. Frankenberg, E. J. Dlugokencky, I. Morino, J. Notholt, V. Sherlock, D. Wunch, V. Beck, C. Gerbig, H. Chen, E. A. Kort, T. Röckmann, and I. Aben
Atmos. Chem. Phys., 14, 3991–4012, https://doi.org/10.5194/acp-14-3991-2014, https://doi.org/10.5194/acp-14-3991-2014, 2014
B. Ringeval, S. Houweling, P. M. van Bodegom, R. Spahni, R. van Beek, F. Joos, and T. Röckmann
Biogeosciences, 11, 1519–1558, https://doi.org/10.5194/bg-11-1519-2014, https://doi.org/10.5194/bg-11-1519-2014, 2014
T. Borsdorff, O. P. Hasekamp, A. Wassmann, and J. Landgraf
Atmos. Meas. Tech., 7, 523–535, https://doi.org/10.5194/amt-7-523-2014, https://doi.org/10.5194/amt-7-523-2014, 2014
R. Locatelli, P. Bousquet, F. Chevallier, A. Fortems-Cheney, S. Szopa, M. Saunois, A. Agusti-Panareda, D. Bergmann, H. Bian, P. Cameron-Smith, M. P. Chipperfield, E. Gloor, S. Houweling, S. R. Kawa, M. Krol, P. K. Patra, R. G. Prinn, M. Rigby, R. Saito, and C. Wilson
Atmos. Chem. Phys., 13, 9917–9937, https://doi.org/10.5194/acp-13-9917-2013, https://doi.org/10.5194/acp-13-9917-2013, 2013
S. Basu, S. Guerlet, A. Butz, S. Houweling, O. Hasekamp, I. Aben, P. Krummel, P. Steele, R. Langenfelds, M. Torn, S. Biraud, B. Stephens, A. Andrews, and D. Worthy
Atmos. Chem. Phys., 13, 8695–8717, https://doi.org/10.5194/acp-13-8695-2013, https://doi.org/10.5194/acp-13-8695-2013, 2013
D. A. Belikov, S. Maksyutov, M. Krol, A. Fraser, M. Rigby, H. Bian, A. Agusti-Panareda, D. Bergmann, P. Bousquet, P. Cameron-Smith, M. P. Chipperfield, A. Fortems-Cheiney, E. Gloor, K. Haynes, P. Hess, S. Houweling, S. R. Kawa, R. M. Law, Z. Loh, L. Meng, P. I. Palmer, P. K. Patra, R. G. Prinn, R. Saito, and C. Wilson
Atmos. Chem. Phys., 13, 1093–1114, https://doi.org/10.5194/acp-13-1093-2013, https://doi.org/10.5194/acp-13-1093-2013, 2013
Related subject area
Subject: Gases | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning
Identifying Drivers of Surface Ozone Bias in Global Chemical Reanalysis with Explainable Machine Learning
Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning
Estimating nitrogen and sulfur deposition across China during 2005 to 2020 based on multiple statistical models
Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025, https://doi.org/10.5194/acp-25-5159-2025, 2025
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We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen
Atmos. Chem. Phys., 25, 685–704, https://doi.org/10.5194/acp-25-685-2025, https://doi.org/10.5194/acp-25-685-2025, 2025
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Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).
Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman
EGUsphere, https://doi.org/10.5194/egusphere-2024-3753, https://doi.org/10.5194/egusphere-2024-3753, 2025
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This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers such as meteorological conditions and precursor emissions were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
Chenliang Tao, Yanbo Peng, Qingzhu Zhang, Yuqiang Zhang, Bing Gong, Qiao Wang, and Wenxing Wang
Atmos. Chem. Phys., 24, 4177–4192, https://doi.org/10.5194/acp-24-4177-2024, https://doi.org/10.5194/acp-24-4177-2024, 2024
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We developed a novel transformer framework to bridge the sparse surface monitoring for inferring ozone–NOx–VOC–aerosol sensitivity and their urban–nonurban discrepancies at a finer scale with implications for improving our understanding of ozone variations. The change in urban–rural disparities in ozone was dominated by PM2.5 from 2019 to 2020. An aerosol-inhibited regime on top of the two traditional NOx- and VOC-limited regimes was identified in Jiaodong Peninsula, Shandong, China.
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, Paul J. Young, and Massimo Vieno
Atmos. Chem. Phys., 24, 3163–3196, https://doi.org/10.5194/acp-24-3163-2024, https://doi.org/10.5194/acp-24-3163-2024, 2024
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High-resolution spatial fields of surface ozone are used to understand spikes in ozone concentration and predict their impact on public health. Such fields are routinely output from complex mathematical models for atmospheric conditions. These outputs are on a coarse spatial resolution and the highest concentrations tend to be biased. Using a novel data-driven machine learning methodology, we show how such output can be corrected to produce fields with both lower bias and higher resolution.
Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 10267–10285, https://doi.org/10.5194/acp-23-10267-2023, https://doi.org/10.5194/acp-23-10267-2023, 2023
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In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
Kaiyue Zhou, Wen Xu, Lin Zhang, Mingrui Ma, Xuejun Liu, and Yu Zhao
Atmos. Chem. Phys., 23, 8531–8551, https://doi.org/10.5194/acp-23-8531-2023, https://doi.org/10.5194/acp-23-8531-2023, 2023
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We developed a dataset of the long-term (2005–2020) variabilities of China’s nitrogen and sulfur deposition, with multiple statistical models that combine available observations and chemistry transport modeling. We demonstrated the strong impact of human activities and national pollution control actions on the spatiotemporal changes in deposition and indicated a relatively small benefit of emission abatement on deposition (and thereby ecological risk) for China compared to Europe and the USA.
Jean-Maxime Bertrand, Frédérik Meleux, Anthony Ung, Gaël Descombes, and Augustin Colette
Atmos. Chem. Phys., 23, 5317–5333, https://doi.org/10.5194/acp-23-5317-2023, https://doi.org/10.5194/acp-23-5317-2023, 2023
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Post-processing methods based on machine learning algorithms were applied to refine the forecasts of four key pollutants at monitoring sites across Europe. Performances show significant improvements compared to those of the deterministic model raw outputs. Taking advantage of the large modelling domain extension, an innovative
globalapproach is proposed to drastically reduce the period necessary to train the models and thus facilitate the implementation in an operational context.
Cited articles
ASI – Agenzia Spaziale Italiana (Italian Space Agency): The PRISMA data portal, https://prismauserregistration.asi.it (last access: 20 April, 2023), 2023. a
Bloom, A., Bowman, K., Lee, M., Turner, A., Schroeder, R., Worden, J., Weidner, R., McDonald, K., and Jacob, D.: CMS: Global 0.5-deg Wetland Methane
Emissions and Uncertainty (WetCHARTs v1.3.1), ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1915, 2021. a, b
Borsdorff, T., Aan De Brugh, J., Hu, H., Hasekamp, O., Sussmann, R., Rettinger, M., Hase, F., Gross, J., Schneider, M., Garcia, O., Stremme, W., Grutter, M., Feist, D. G., Arnold, S. G., De Mazière, M., Kumar Sha, M., Pollard, D. F., Kiel, M., Roehl, C., Wennberg, P. O., Toon, G. C., and Landgraf, J.: Mapping carbon monoxide pollution from space down to city scales with daily global coverage, Atmos. Meas. Tech., 11, 5507–5518,
https://doi.org/10.5194/amt-11-5507-2018, 2018. a
Breiman, L.: Random Forests, Mach. Learn. 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Chen, Z., Jacob, D. J., Nesser, H., Sulprizio, M. P., Lorente, A., Varon,
D. J., Lu, X., Shen, L., Qu, Z., Penn, E., and Yu, X.: Methane emissions
from China: a high-resolution inversion of TROPOMI satellite observations,
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, 2022. a, b
Cheng, G., Xie, X., Han, J., Guo, L., and Xia, G. S.: Remote Sensing Image
Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks,
and Opportunities, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 13, 3735–3756, https://doi.org/10.1109/JSTARS.2020.3005403, 2020. a, b, c, d
Chollet, F.: Deep Learning with Python, Manning, ISBN 13:978-1617296864, 2021. a
Chollet, F., et al.: Keras, https://keras.io (last access: 20 April 2023), 2015. a
Cogliati, S., Sarti, F., Chiarantini, L., Cosi, M., Lorusso, R., Lopinto, E.,
Miglietta, F., Genesio, L., Guanter, L., Damm, A., Pérez-López,
S., Scheffler, D., Tagliabue, G., Panigada, C., Rascher, U., Dowling, T. P.,
Giardino, C., and Colombo, R.: The PRISMA imaging spectroscopy mission:
overview and first performance analysis, Remote Sens. Environ., 262,
112499, https://doi.org/10.1016/j.rse.2021.112499, 2021. a, b, c
Cohen, J.: A Coefficient of Agreement for Nominal Scales, Educat. Psychol. Meas., 20, 37–46, https://doi.org/10.1177/001316446002000104, 1960. a
Contributors to the WRF repository: The official repository for the Weather Research and Forecasting (WRF) model, GitHub [code], https://github.com/wrf-model/WRF/releases/ (last access: 20 April 2023), 2023. a
Copernicus Climate Change Service: Climate Data Store,
https://cds.climate.copernicus.eu/ (last access: 20 April 2023), 2023. a
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E., Monforti-Ferrario, F., Olivier, J., and Vignati, E.: EDGAR v6.0 Greenhouse Gas Emissions [Dataset], European Commission, Joint Research Centre (JRC) [data set], http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b (last access: 20 April 2023), 2021. a, b
Cusworth, D. H., Jacob, D. J., Varon, D. J., Chan Miller, C., Liu, X., Chance, K., Thorpe, A. K., Duren, R. M., Miller, C. E., Thompson, D. R., Frankenberg, C., Guanter, L., and Randles, C. A.: Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space, Atmos. Meas. Tech., 12, 5655–5668, https://doi.org/10.5194/amt-12-5655-2019, 2019. a
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Tseng, E., Thompson, D., Guha,
A., Newman, S., Foster, K. T., and Miller, C. E.: Using remote sensing to
detect, validate, and quantify methane emissions from California solid waste
operations, Environ. Res. Lett., 15, 054012, https://doi.org/10.1088/1748-9326/ab7b99, 2020. a
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Pandey, S., Maasakkers, J. D.,
Aben, I., Jervis, D., Varon, D. J., Jacob, D. J., Randles, C. A., Gautam, R.,
Omara, M., Schade, G. W., Dennison, P. E., Frankenberg, C., Gordon, D.,
Lopinto, E., and Miller, C. E.: Multisatellite Imaging of a Gas Well Blowout
Enables Quantification of Total Methane Emissions, Geophys. Res. Lett., 48, e2020GL090864, https://doi.org/10.1029/2020GL090864, 2021. a, b, c, d
de Gouw, J. A., Veefkind, J. P., Roosenbrand, E., Dix, B., Lin, J. C.,
Landgraf, J., and Levelt, P. F.: Daily Satellite Observations of Methane
from Oil and Gas Production Regions in the United States, Sci. Rep., 10, 1–10, https://doi.org/10.1038/s41598-020-57678-4, 2020. a
Dekker, I. N., Houweling, S., Aben, I., Röckmann, T., Krol, M.,
Martínez-Alonso, S., Deeter, M. N., and Worden, H. M.: Quantification of
CO emissions from the city of Madrid using MOPITT satellite retrievals and
WRF simulations, Atmos. Chem. Phys., 17, 14675–14694,
https://doi.org/10.5194/acp-17-14675-2017, 2017. a
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F.,
Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F.,
Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's Optical
High-Resolution Mission for GMES Operational Services, Remote Sens. Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012. a, b
Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav,
V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K., Frankenberg, C.,
McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E.,
Green, R. O., and Miller, C. E.: California's methane super-emitters,
Nature, 575, 180–184, https://doi.org/10.1038/s41586-019-1720-3, 2019. a
ESA: GHGSat: GHGSat mission overview, ESA,
https://earth.esa.int/eogateway/missions/ghgsat (last access: 20 April 2023), 2022. a
ESA: Copernicus Open Access Hub, https://scihub.copernicus.eu/,
(last access: 20 April 2023), 2023. a
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, 226–231, https://aaai.org/ (last access: 20 April 2023), 1996. a
European Commission: Launch by United States, the European Union, and
Partners of the Global Methane Pledge to Keep 1.5 ∘C Within Reach,
https://ec.europa.eu/commission/presscorner/detail/en/statement_21_5766
(last access: 20 April 2023), 2021. a
Finch, D. P., Palmer, P. I., and Zhang, T.: Automated detection of atmospheric NO2 plumes from satellite data: A tool to help infer anthropogenic combustion emissions, Atmos. Meas. Tech., 15, 721–733,
https://doi.org/10.5194/amt-15-721-2022, 2022. a
Frankenberg, C., Thorpe, A. K., Thompson, D. R., Hulley, G., Kort, E. A.,
Vance, N., Borchardt, J., Krings, T., Gerilowski, K., Sweeney, C., Conley,
S., Bue, B. D., Aubrey, A. D., Hook, S., and Green, R. O.: Airborne methane
remote measurements reveal heavytail flux distribution in Four Corners
region, P. Natl. Acad. Sci. USA, 113, 9734–9739, https://doi.org/10.1073/pnas.1605617113, 2016. a, b, c
GMAO, The Global Modeling and Assimilation Office, and NASA: GMAO data products, https://gmao.gsfc.nasa.gov/GMAO_products/ (last access: 20 April 2023), 2023. a
Gorroño, J., Varon, D. J., Irakulis-Loitxate, I., and Guanter, L.: Understanding the potential of Sentinel-2 for monitoring methane point emissions, Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, 2023. a, b, c
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975,
https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Guanter, L., Irakulis-Loitxate, I., Gorroño, J.,
Sánchez-García, E., Cusworth, D. H., Varon, D. J., Cogliati, S.,
and Colombo, R.: Mapping methane point emissions with the PRISMA spaceborne
imaging spectrometer, Remote Sens. Environ., 265, 112671,
https://doi.org/10.1016/j.rse.2021.112671, 2021. a, b, c, d
Hasekamp, O., Lorente, A., Hu, H., Butz, A., Aan de Brugh, J., and Landgraf,
J.: Algorithm Theoretical Baseline Document for Sentinel-5 Precursor methane
Retrieval, SRON The Netherlands Institute for Space Research, Leiden, the
Netherlands,
https://sentinels.copernicus.eu/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-Methane-retrieval.pdf/f275eb1d-89a8-464f-b5b8-c7156cda874e?t=1658313508597
(last access: 20 April 2023), 2022. a, b
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Los Alamitos, CA, USA, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5
global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hu, H., Landgraf, J., Detmers, R., Borsdorff, T., Aan de Brugh, J., Aben, I.,
Butz, A., and Hasekamp, O.: Toward Global Mapping of Methane With TROPOMI:
First Results and Intersatellite Comparison to GOSAT, Geophy. Res. Lett., 45, 3682–3689, https://doi.org/10.1002/2018GL077259, 2018. a
IPCC: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021. a, b
Iqbal, H.: HarisIqbal88/PlotNeuralNet v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.2526396, 2018. a
Irakulis-Loitxate, I., Gorroño, J., Zavala-Araiza, D., and Guanter, L.:
Satellites Detect a Methane Ultra-emission Event from an Offshore Platform in
the Gulf of Mexico, Environ. Sci. Technol. Lett., 9, 520–525,
https://doi.org/10.1021/acs.estlett.2c00225, 2022a. a
Irakulis-Loitxate, I., Guanter, L., Maasakkers, J. D., Zavala-Araiza, D., and
Aben, I.: Satellites Detect Abatable Super-Emissions in One of the World’s
Largest Methane Hotspot Regions, Environ. Sci. Technol., 56, 2143–2152, https://doi.org/10.1021/acs.est.1c04873, 2022b. a, b, c, d
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X.,
Chance, K., Aben, I., McKeever, J., and Frankenberg, C.: Satellite
observations of atmospheric methane and their value for quantifying methane
emissions, Atmos. Chem. Phys, 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, 2016. a, b, c
Jacob, D. J., Varon, D. J., Cusworth, D. H., Dennison, P. E., Frankenberg, C., Gautam, R., Guanter, L., Kelley, J., McKeever, J., Ott, L. E., Poulter, B., Qu, Z., Thorpe, A. K., Worden, J. R., and Duren, R. M.: Quantifying methane emissions from the global scale down to point sources using satellite
observations of atmospheric methane, Atmos. Chem. Phys., 22,
9617–9646, https://doi.org/10.5194/acp-22-9617-2022, 2022. a, b, c
Jervis, D., McKeever, J., Durak, B. O., Sloan, J. J., Gains, D., Varon, D. J., Ramier, A., Strupler, M., and Tarrant, E.: The GHGSat-D imaging
spectrometer, Atmos. Meas. Tech., 14, 2127–2140,
https://doi.org/10.5194/amt-14-2127-2021, 2021. a, b
Johnson, J. M. and Khoshgoftaar, T. M.: Survey on deep learning with class
imbalance, J. Big Data, 6, 27, https://doi.org/10.1186/s40537-019-0192-5, 2019. a, b, c
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization,
arxiv [preprint], http://arxiv.org/abs/1412.6980 (last access: 20 April 2023), 2014. a
Lauvaux, T., Giron, C., Mazzolini, M., D'aspremont, A., Duren, R., and
Cusworth, D.: Global assessment of oil and gas methane ultra-emitters,
Science, 375, 557–561, https://doi.org/10.1126/science.abj4351, 2022. a, b, c, d
Li, L., Jamieson, K., Rostamizadeh, A., and Talwalkar, A.: Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization, J. Mach. Learn. Res., 18, 1–52, 2018. a
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., Aan De Brugh, J.,
Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi,
K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke,
T., and Landgraf, J.: Methane retrieved from TROPOMI: Improvement of the
data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021. a, b, c
Lorente, A., Borsdorff, T., Landgraf, J., and SRON L2 team: TROPOMI scientific
Maasakkers, J. D., Omara, M., Gautam, R., Lorente, A., Pandey, S., Tol, P.,
Borsdorff, T., Houweling, S., and Aben, I.: Reconstructing and quantifying
methane emissions from the full duration of a 38-day natural gas well blowout
using space-based observations, Remote Sens. Environ., 270, 112755, https://doi.org/10.1016/j.rse.2021.112755, 2022a. a, b, c
Maasakkers, J. D., Varon, D. J., Elfarsdóttir, A., McKeever, J., Jervis, D., Mahapatra, G., Pandey, S., Lorente, A., Borsdorff, T., Foorthuis, L. R.,
Schuit, B. J., Tol, P., van Kempen, T. A., van Hees, R., and Aben, I.: Using
satellites to uncover large methane emissions from landfills, Sci. Adv, 8,
9683, https://doi.org/10.1126/sciadv.abn9683, 2022b. a, b, c, d, e, f, g, h, i, j
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and Eichmann,
A.: The GEOS-5 Atmospheric General Circulation Model: Mean Climate and
Development from MERRA to Fortuna, Technical Report Series on Global Modeling
and Data Assimilation, Tech. Rep., https://ntrs.nasa.gov/citations/20120011790 (last access: 25 July 2023), 2012. a, b, c, d, e, f, g, h, i, j
Nisbet, E. G., Fisher, R. E., Lowry, D., France, J. L., Allen, G., Bakkaloglu, S., Broderick, T. J., Cain, M., Coleman, M., Fernandez, J., Forster, G., Griffiths, P. T., Iverach, C. P., Kelly, B. F., Manning, M. R., Nisbet-Jones, P. B., Pyle, J. A., Townsend-Small, A., al Shalaan, A., Warwick, N., and Zazzeri, G.: Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement, Rev. Geophys., 58, e2019RG000675, https://doi.org/10.1029/2019RG000675, 2020. a, b, c
NOAA: Trends in Atmospheric Methane, NOAA,
https://gml.noaa.gov/ccgg/trends_ch4/ (last access: 20 April 2023), 2022. a
Ocko, I. B., Naik, V., and Paynter, D.: Rapid and reliable assessment of
methane impacts on climate, Atmos. Chem. Phys., 18, 15555–15568, https://doi.org/10.5194/acp-18-15555-2018, 2018. a
Ocko, I. B., Sun, T., Shindell, D., Oppenheimer, M., Hristov, A. N., Pacala,
S. W., Mauzerall, D. L., Xu, Y., and Hamburg, S. P.: Acting rapidly to
deploy readily available methane mitigation measures by sector can
immediately slow global warming, Environ. Res. Lett., 16, 054042, https://doi.org/10.1088/1748-9326/abf9c8, 2021. a, b
O'Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L.,
et al.: KerasTuner, GitHub [code], https://github.com/keras-team/keras-tuner (last access: 20 April 2023), 2019. a
Pandey, S., Gautam, R., Houweling, S., Denier Van Der Gon, H., Sadavarte, P.,
Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., Van Kempen, T.,
Hoogeveen, R., Van Hees, R., Hamburg, S. P., Maasakkers, J. D., and Aben, I.:
Satellite observations reveal extreme methane leakage from a natural gas
well blowout, P. Natl. Acad. Sci. USA, 116, 26376–26381, https://doi.org/10.1073/pnas.1908712116, 2019. a, b
Pandey, S., Houweling, S., Lorente, A., Borsdorff, T., Tsivlidou, M.,
Anthony Bloom, A., Poulter, B., Zhang, Z., and Aben, I.: Using satellite
data to identify the methane emission controls of South Sudan's wetlands,
Biogeosciences, 18, 557–572, https://doi.org/10.5194/bg-18-557-2021, 2021. a, b
Parry, I., Black, S., Minnett, D., Mylonas, V., and Vernon, N.: How to Cut
Methane Emissions; IMF Staff Climate Note 2022/008, October 2022, Tech. rep., International Monetary Fund, https://www.imf.org/en/Publications/staff-climate-notes/Issues/2022/10/28/How-to-Cut-Methane-Emissions-525188 (last access: 20 April 2023), 2022. a
Plant, G., Kort, E. A., Brandt, A. R., Chen, Y., Fordice, G., Gorchov Negron,
A. M., Schwietzke, S., Smith, M., and Zavala-Araiza, D.: Inefficient and
unlit natural gas flares both emit large quantities of methane, Science,
377, 1566–1571, https://doi.org/10.1126/science.abq0385, 2022. a, b
Qu, Z., Jacob, D. J., Shen, L., Lu, X., Zhang, Y., Scarpelli, T. R., Nesser,
H., Sulprizio, M. P., Maasakkers, J. D., Bloom, A. A., Worden, J. R., Parker,
R. J., and Delgado, A. L.: Global distribution of methane emissions: A
comparative inverse analysis of observations from the TROPOMI and GOSAT
satellite instruments, Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, 2021. a
Ramier, A., Deglint, H., Gains, D., Jervis, D., McKeever, J., Shaw, W.,
Strupler, M., Tarrant, E., and Varon, D. J.: GHGSat-C1-Initial Results,
Design, and Characterization, in: vol. 2020, AGU Fall Meeting Abstracts, 1–17 December 2020, virtual, A247-03, https://ui.adsabs.harvard.edu/abs/2020AGUFMA247...03R/abstract (last access: 20 April 2023), 2020. a, b
Sadavarte, P., Pandey, S., Maasakkers, J. D., Lorente, A., Borsdorff, T.,
Denier van der Gon, H., Houweling, S., and Aben, I.: Methane Emissions from
Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite
Observations, Environ. Sci. Technol., 55, 16573–16580,
https://doi.org/10.1021/acs.est.1c03976, 2021. a, b, c
Sánchez-García, E., Gorroño, J., Irakulis-Loitxate, I., Varon, D. J., and Guanter, L.: Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite, Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, 2022. a
Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020. a, b, c
Scarpelli, T. R. and Jacob, D. J.: Global Fuel Exploitation Inventory (GFEI),
Harvard Dataverse [data set], https://doi.org/10.7910/DVN/HH4EUM, 2022. a
Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M. P., Zhang, Y., Reuland, F., Gordon, D., and Worden, J. R.: Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: Evaluation with inversions of atmospheric methane observations, Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, 2022a. a
Schneising, O., Buchwitz, M., Reuter, M., Vanselow, S., Bovensmann, H., and
Burrows, P. J.: Remote sensing of methane leakage from natural gas and
petroleum systems revisited, Atmos. Chem. Phys., 20, 9169–9182, https://doi.org/10.5194/acp-20-9169-2020, 2020. a, b
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., and Xu, X.: DBSCAN
revisited, revisited: Why and how you should (still) use DBSCAN, ACM
T. Database Syst., 42, 19, https://doi.org/10.1145/3068335, 2017. a
Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., Van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data] (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8087134, 2023a. a
Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., Van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Interactive map with TROPOMI and high-resolution scenes [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data] (1.0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.8355808, 2023b. a
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra,
D.: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based
Localization, Int. J. Comput. Vis., 128, 336–359, https://doi.org/10.1007/s11263-019-01228-7, 2020. a, b, c, d
Shaw, J. T., Allen, G., Barker, P., Pitt, J. R., Pasternak, D., Bauguitte,
S. J., Lee, J., Bower, K. N., Daly, M. C., Lunt, M. F., Ganesan, A. L.,
Vaughan, A. R., Chibesakunda, F., Lambakasa, M., Fisher, R. E., France,
J. L., Lowry, D., Palmer, P. I., Metzger, S., Parker, R. J., Gedney, N.,
Bateson, P., Cain, M., Lorente, A., Borsdorff, T., and Nisbet, E. G.: Large
Methane Emission Fluxes Observed From Tropical Wetlands in Zambia, Global
Biogeochem. Cy., 36, e2021GB007261, https://doi.org/10.1029/2021GB007261, 2022. a
Shen, L., Gautam, R., Omara, M., Zavala-Araiza, D., Maasakkers, J. D.,
Scarpelli, T. R., Lorente, A., Lyon, D., Sheng, J., Varon, D. J., Nesser, H.,
Qu, Z., Lu, X., Sulprizio, M. P., Hamburg, S. P., and Jacob, D. J.:
Satellite quantification of oil and natural gas methane emissions in the US
and Canada including contributions from individual basins, Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, 2022. a, b
Sherwin, E. D., Rutherford, J. S., Chen, Y., Aminfard, S., Kort, E. A.,
Jackson, R. B., and Brandt, A. R.: Single-blind validation of space-based
point-source methane emissions detection and quantification, Sci. Rep., 13, 3836, https://doi.org/10.1038/s41598-023-30761-2, 2023. a, b, c
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for
Large-Scale Image Recognition, arxiv [preprint], https://doi.org/10.48550/ARXIV.1409.1556, 2014. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J.,
Wang, W., Powers, J. G., Duda, M. G., and Barker, D. M.: A description of
the advanced research WRF model version 4, Tech. rep., National Center for
Atmospheric Research, Boulder, CO, USA, https://doi.org/10.5065/1dfh-6p97, 2019. a, b
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014. a
SRON CH4 L2 team: SRON RemoTeC-S5P scientific
Szopa, S., Naik, V., Adhikary, B., Artaxo, P., Berntsen, T., Collins, W.,
Fuzzi, S., Gallardo, L., Kiendler-Scharr, A., Klimont, Z., Liao, H., Unger, N., and Zanis, P.: Short-Lived Climate Forcers, Cambridge University Press, Cambridge, UK and New York, NY, USA, 817–922, https://doi.org/10.1017/9781009157896.008, 2021. a, b
Tu, Q., Hase, F., Schneider, M., Garciá, O., Blumenstock, T., Borsdorff, T., Frey, M., Khosrawi, F., Lorente, A., Alberti, C., Bustos, J. J., Butz,
A., Carreño, V., Cuevas, E., Curcoll, R., Diekmann, C. J., Dubravica, D., Ertl, B., Estruch, C., León-Luis, S. F., Marrero, C., Morgui, J. A., Ramos, R., Scharun, C., Schneider, C., Sepúlveda, E., Toledano, C., and Torres, C.: Quantification of CH4 emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASI, Atmos. Chem. Phys., 22, 295–317, https://doi.org/10.5194/acp-22-295-2022, 2022a. a, b
Tu, Q., Schneider, M., Hase, F., Khosrawi, F., Ertl, B., Necki, J., Dubravica, D., Diekmann, C. J., Blumenstock, T., and Fang, D.: Quantifying CH4 emissions in hard coal mines from TROPOMI and IASI observations using the wind-assigned anomaly method, Atmos. Chem. Phys., 22, 9747–9765, https://doi.org/10.5194/acp-22-9747-2022, 2022b. a, b
Valade, S., Ley, A., Massimetti, F., D'Hondt, O., Laiolo, M., Coppola, D.,
Loibl, D., Hellwich, O., and Walter, T. R.: Towards global volcano
monitoring using multisensor sentinel missions and artificial intelligence:
The MOUNTS monitoring system, Remote Sens., 11, 1–31, https://doi.org/10.3390/rs11131528, 2019. a
Varon, D.: Replication Data for: Automated detection and monitoring of methane super-emitters using satellite data, Harvard Dataverse [data set],
https://doi.org/10.7910/DVN/QQQ9IU, 2022. a
Varon, D. J., McKeever, J., Jervis, D., Maasakkers, J. D., Pandey, S.,
Houweling, S., Aben, I., Scarpelli, T., and Jacob, D. J.: Satellite
Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production, Geophys. Res. Lett., 46, 13507–13516, https://doi.org/10.1029/2019GL083798, 2019. a, b, c, d, e, f
Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J.,
Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele,
M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann,
P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.:
TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global
observations of the atmospheric composition for climate, air quality and
ozone layer applications, Remote Sens. Environ., 120, 70–83,
https://doi.org/10.1016/j.rse.2011.09.027, 2012.
a
Yu, J., Hmiel, B., Lyon, D. R., Warren, J., Cusworth, D. H., Duren, R. M.,
Chen, Y., Murphy, E. C., and Brandt, A. R.: Methane Emissions from Natural
Gas Gathering Pipelines in the Permian Basin, Environ. Sci. Technol. Lett., 9, 969–974, https://doi.org/10.1021/acs.estlett.2c00380, 2022. a
Zavala-Araiza, D., Lyon, D., Ramón, R., Alvarez, A., Palacios, V., Harriss,
R., Lan, X., Talbot, R., and Hamburg, S. P.: Toward a Functional Definition
of Methane Super-Emitters: Application to Natural Gas Production Sites,
Environ. Sci. Technol., 49, 8167–8174, https://doi.org/10.1021/acs.est.5b00133, 2015. a, b
Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., Lyon, D., Nesser, H., Sulprizio, M. P., Varon, D. J., Zhang, R., Houweling, S., Zavala-Araiza, D., Alvarez, R. A., Lorente, A., Hamburg, S. P., Aben, I., and Jacob, D. J.: Quantifying methane emissions from the largest oil-producing basin in the United States from space, Sci. Adv., 6,
1–10, https://doi.org/10.1126/sciadv.aaz5120, 2020. a, b
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A.: Learning
Deep Features for Discriminative Localization, arxiv [preprint],
http://arxiv.org/abs/1512.04150 (last access: 20 April 2023), 2015. a
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Using two machine learning models, which were trained on TROPOMI methane satellite data, we...
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